Calculating a fractional flow reserve

ABSTRACT

A method for vascular assessment is disclosed. The method, in some embodiments, comprises receiving a plurality of 2-D angiographic images of a portion of a vasculature of a subject, and processing the images to produce a stenotic model over the vasculature, the stenotic model having measurements of the vasculature at one or more locations along vessels of the vasculature. The method, in some embodiments, further comprises obtaining a flow characteristic of the stenotic model, and calculating an index indicative of vascular function, based, at least in part, on the flow characteristic in the stenotic model.

PRIORITY CLAIM

This application claims priority to and the benefit as a continuationapplication of U.S. patent application Ser. No. 16/826,594, filed onMar. 23, 2020, which is a continuation application of U.S. patentapplication Ser. No. 14/761,086, filed on Jul. 15, 2015, now U.S. Pat.No. 10,595,807, which is a National Phase of PCT Patent Application No.PCT/IL2014/050043 having an International filing date of Jan. 15, 2014,which is a Continuation-in-Part (CIP) of PCT Patent Application No.PCT/IL2013/050869, having an International filing date of Oct. 24, 2013,which claims the benefit of priority under 35 USC § 119(e) of U.S.Provisional Patent Application No. 61/717,732, filed on Oct. 24, 2012.PCT Patent Application No. IL2014/050043 is also a Continuation-in-Part(CIP) of U.S. patent application Ser. No. 14/040,688, filed on Sep. 29,2013, now U.S. Pat. No. 9,858,387, which claims the benefit of priorityunder 35 USC § 119(e) of U.S. Provisional Patent Application No.61/752,526, filed on Jan. 15, 2013. The contents of the aboveapplications are all incorporated by reference as if fully set forthherein in their entirety.

PCT Patent Application No. PCT/IL2014/050043 comprises one of threeco-filed PCT Patent Applications, namely Nos. IL2014/050039,IL2014/050043, and IL2014/050044, all having an International filingdate of Jan. 15, 2014.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to vascularmodeling, and, more particularly, but not exclusively, to the use of avascular model for producing indices relating to vascular function anddiagnosis in real time—for example, during a catheterized imagingprocedure.

Arterial stenosis is one of the most serious forms of arterial disease.In clinical practice, stenosis severity is estimated by using eithersimple geometrical parameter, such as determining the percent diameterof a stenosis, or by measuring hemodynamically based parameters, such asthe pressure-based myocardial Fractional Flow Reserve (FFR). FFR is aninvasive measurement of the functional significance of coronarystenoses. The FFR measurement technique involves insertion of a 0.014″guidewire equipped with a miniature pressure transducer located acrossthe arterial stenosis. It represents the ratio between the maximal bloodflow in the area of stenosis and the maximal blood flow in the sameterritory without stenosis. Earlier studies showed that FFR<0.75 is anaccurate predictor of ischemia and deferral of percutaneous coronaryintervention for lesions with FFR>0.75 appeared to be safe.

An FFR cut-off value of 0.8 is typically used in clinical practice toguide revascularization, supported by long-term outcome data. Typically,an FFR value in a range of 0.75-0.8 is considered a ‘grey zone’ havinguncertain clinical significance.

Modeling vascular flow and assessing vascular flow is described, forexample, in U.S. Patent Application Publication No. 2012/0059246 ofTaylor, to a “Method And System For Patient-Specific Modeling Of BloodFlow”, which describes embodiments which include a system fordetermining cardiovascular information for a patient. The system mayinclude at least one computer system configured to receivepatient-specific data regarding a geometry of at least a portion of ananatomical structure of the patient. The portion of the anatomicalstructure may include at least a portion of the patient's aorta and atleast a portion of a plurality of coronary arteries emanating from theportion of the aorta. The at least one computer system may also beconfigured to create a three-dimensional model representing the portionof the anatomical structure based on the patient-specific data, create aphysics-based model relating to a blood flow characteristic within theportion of the anatomical structure, and determine a fractional flowreserve within the portion of the anatomical structure based on thethree-dimensional model and the physics-based model. Additionalbackground art includes:

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The disclosures of all references mentioned above and throughout thepresent specification, as well as the disclosures of all referencesmentioned in those references, are hereby incorporated herein byreference.

SUMMARY OF THE INVENTION

According to an aspect of some embodiments of the present invention,there is provided a method for vascular assessment comprising: receivinga first vascular model of a cardiac vasculature; determining at leastone characteristic based on the first vascular model representing flowthrough a stenotic segment of the vasculature; generating a secondvascular model, comprising elements corresponding to the first vascularmodel, and at least one modification including a difference in at leastone characteristic of flow; and calculating a flow index comparing thefirst and the second model.

According to some embodiments of the invention, the difference in atleast one characteristic of flow comprises a difference between at leastone characteristic of flow through a stenotic segment, and acharacteristic of flow in a corresponding segment of the second model.

According to some embodiments of the invention, the vascular model iscalculated based on a plurality of 2-D angiographic images.

According to some embodiments of the invention, the angiographic imagesare of sufficient resolution to allow determination of vascular widthwithin 10%, to a vessel segment following an at least third branch pointfrom a main human coronary artery.

According to some embodiments of the invention, the flow index comprisesa prediction of flow increase achievable by an intervention to removestenosis from the stenotic segment.

According to some embodiments of the invention, the comparative flowindex is calculated based on a ratio of corresponding flowcharacteristics of the first and second vascular models.

According to some embodiments of the invention, the comparative flowindex is calculated based on a ratio of corresponding flowcharacteristics of the stenotic and astenotic segments.

According to some embodiments of the invention, the method comprisesreporting the comparative flow index as a single number per stenosis.

According to some embodiments of the invention, the at least onecharacteristic of flow comprises a flow rate.

According to some embodiments of the invention, the comparative flowindex comprises an index representing a Fractional Flow Reserve indexcomprising a ratio of the maximal flow through a stenotic vessel, to themaximal flow through the stenotic vessel with the stenosis removed.

According to some embodiments of the invention, the comparative flowindex is used in determining a recommendation for revascularization.

According to some embodiments of the invention, the comparative flowindex comprises a value indicating a capacity for restoring flow byremoval of a stenosis.

According to some embodiments of the invention, the first and the secondvascular models comprise connected branches of vascular segment data,each branch being associated with a corresponding vascular resistance toflow.

According to some embodiments of the invention, the vascular model doesnot include a radially detailed 3-D description of the vascular wall.

According to some embodiments of the invention, the second vascularmodel is a normal model, comprising a relatively enlarged-diametervessel replacing a stenotic vessel in the first vascular model.

According to some embodiments of the invention, the second vascularmodel is a normal model, comprising a normalized vessel obtained bynormalizing a stenotic vessel based on properties of a neighboringastenotic vessel.

According to some embodiments of the invention, the at least onecharacteristic of flow is calculated based on properties of a pluralityof vascular segments in flowing connection with the stenotic segment.

According to some embodiments of the invention, the characteristic offlow comprises resistance to fluid flow.

According to some embodiments of the invention, the method comprises:identifying in the first vascular model a stenosed vessel and a crown ofvascular branches downstream of the stenosed vessel, and calculating theresistance to fluid flow in the crown; wherein the flow index iscalculated based on a volume of the crown, and based on a contributionof the stenosed vessel to the resistance to fluid flow.

According to some embodiments of the invention, the first vascular modelcomprises a representation of vascular positions in a three-dimensionalspace.

According to some embodiments of the invention, each vascular modelcorresponds to a portion of the vasculature which is between twoconsecutive bifurcations of the vasculature.

According to some embodiments of the invention, each vascular modelcorresponds to a portion of the vasculature which includes a bifurcationof the vasculature.

According to some embodiments of the invention, each vascular modelcorresponds to a portion of the vasculature which extends at least onebifurcation of the vasculature beyond the stenotic segment.

According to some embodiments of the invention, each vascular modelcorresponds to a portion of the vasculature which extends at least threebifurcations of the vasculature beyond the stenotic segment.

According to some embodiments of the invention, the vascular modelcomprises paths along vascular segments, each of the paths being mappedalong its extent to positions in the plurality of 2-D images.

According to some embodiments of the invention, the method comprisesacquiring images of the cardiac vasculature, and constructing a firstvascular model thereof.

According to some embodiments of the invention, each vascular modelcorresponds to a portion of the vasculature which extends distally asfar as resolution of the images allows determination of vascular widthwithin 10% of the correct value.

According to some embodiments of the invention, the vascular model is ofa vasculature which has been artificially dilated during acquisition ofimages used to generate the model.

According to an aspect of some embodiments of the present invention,there is provided a computer software product, comprising acomputer-readable medium in which program instructions are stored, whichinstructions, when read by a computer, cause the computer to receive aplurality of 2-D images of a subject's vasculature and execute themethod for vascular assessment.

According to an aspect of some embodiments of the present invention,there is provided a system for vascular assessment comprising computerconfigured to:

receive the plurality of 2-D images; convert the plurality of 2-D to afirst vascular model of the vasculature; determine at least onecharacteristic based on the first vascular model representing flowthrough a stenotic segment of the vasculature; generate a secondvascular model, comprising elements corresponding to the first vascularmodel, and at least one modification including altering the at least onecharacteristic of flow through a stenotic segment to a characteristic offlow as if through a corresponding segment in which the effect ofstenosis is reduced, and calculate a flow index comparing the first andthe second model.

According to some embodiments of the invention, the computer isconfigured to calculate the flow index within 5 minutes of receiving thefirst vascular model.

According to some embodiments of the invention, the computer isconfigured to calculate the flow index within 5 minutes of theacquisition of the 2-D images.

According to some embodiments of the invention, the computer is locatedat a location remote from the imaging device.

According to an aspect of some embodiments of the present invention,there is provided a method for vascular assessment comprising: receivinga vascular model of a cardiac vasculature; determining at least a firstflow characteristic based on the vascular model representing flowthrough a stenotic segment of the vasculature and the crown vessels tothe stenotic segment; determining at least a second flow characteristicbased on the vascular model representing flow through the crown vessels,without limitation of the flow by the stenotic segment; and calculatinga flow index comparing the first and the second flow characteristics.

According to an aspect of some embodiments of the present invention,there is provided a method for construction of a vascular tree modelcomprising: receiving a plurality of 2-D angiographic images of bloodvessel segments comprised in a portion of a vasculature of a subject;extracting automatically, from each of the plurality of 2-D angiographicimages, a corresponding image feature set comprising 2-D featurepositions of the blood vessel segments; adjusting automatically the 2-Dfeature positions to reduce relative position error in a common 3-Dcoordinate system to which each the image feature set isback-projectable; associating automatically the 2-D feature positionsacross the image feature sets such that image features projected from acommon blood vessel segment region are associated; and determiningautomatically a representation of the image features based on inspectionof 3-D projections determined from the associated 2-D feature positions,and selection of an optimal available 3-D projection therefrom.

According to some embodiments of the invention, the image feature setwhich is extracted comprises a centerline data set including 2-Dcenterline positions ordered along the blood vessel segments.

According to some embodiments of the invention, the determinedrepresentation is a 3-D spatial representation of blood vessel segmentextent.

According to some embodiments of the invention, the determinedrepresentation is a graph representation of blood vessel segment extent.

According to some embodiments of the invention, information required forthe associating automatically of 2-D image positions is entirelyprovided before review of the images by a human operator.

According to some embodiments of the invention, the adjusting,associating and determining are performed with elements of thecenterline data set.

According to some embodiments of the invention, the adjusting comprisesregistration of the 2-D images in 3-D space according to parameterswhich bring the 2-D centerline positions into closer correspondenceamong their 3-D back-projections.

According to some embodiments of the invention, the image feature setwhich is extracted comprises a landmark data set including at least oneof a group consisting of an origin of the tree model, a location oflocally reduced radius in a stenosed blood vessel segment, and abifurcation among blood vessel segments.

According to some embodiments of the invention, the image feature setwhich is extracted comprises a landmark data set including pixelintensity configurations which are below a predetermined threshold ofself-similarity over translation.

According to some embodiments of the invention, the adjusting isperformed on elements of the landmark data set; and the associating anddetermining are performed among elements of the centerline data set.

According to some embodiments of the invention, the adjusting comprisesregistration of the 2-D images in 3-D space according to parameterswhich bring features of the landmark data set into closer correspondenceamong their 3-D back-projections.

According to some embodiments of the invention, the registration of the2-D images comprises registration of positions of elements of thecenterline data set.

According to some embodiments of the invention, the method comprisesestimating a metric of radial vascular width based on values of at leastone of the plurality of 2-D angiographic images along linesperpendicular to the ordered 2-D centerline positions.

According to some embodiments of the invention, the estimating a metricof radial vascular width comprises finding connected routes runningalong either side of the 2-D centerline positions, and the connectedroutes comprise pixels imaging the boundary region of a vascular wall.

According to some embodiments of the invention, the boundary region of avascular wall is determined by analysis of the intensity gradient alongthe perpendicular lines.

According to some embodiments of the invention, the metric of radialvascular width is calculated as a function of centerline position.

According to some embodiments of the invention, the determiningcomprises adjusting of the 2-D feature positions based on projection ofthe 3-D representation into the 2-D plane of at least one of theplurality of 2-D angiographic images.

According to some embodiments of the invention, the adjusting comprises:calculating a 3-D representation of feature positions from the 2-Dfeature positions of a first subset of the plurality of 2-D angiographicimages; adjusting 2-D feature positions in a second subset of theplurality of 2-D angiographic images to more closely match features ofthe 3-D representation, as if the first 3-D representation wereprojected into the adjusted imaging planes of the second subset; anditerating over the calculating and the adjusting with changes to thefirst and second subsets, until a halt condition is met.

According to some embodiments of the invention, the halt condition is alack of position adjusting to the 2-D feature positions above a distancethreshold.

According to some embodiments of the invention, the method comprisesdefining a surface corresponding to a shape of the heart of the subject,and using the surface as a constraint for the associating of the featurepositions.

According to some embodiments of the invention, the images are acquiredupon injection of a contrast agent to the vasculature, and the methodfurther comprises: determining temporal characteristics of the movementof the contrast agent through the vasculature; constraining the featurepositions based on the temporal characteristics.

According to some embodiments of the invention, the portion of thevasculature comprises coronary arteries.

According to some embodiments of the invention, the capturing of theplurality of 2D angiographic images is effected by a plurality ofimaging devices to capture the plurality of 2D angiographic images.

According to some embodiments of the invention, the capturing of theplurality of 2D angiographic images comprises synchronizing theplurality of imaging devices to capture the plurality of imagessubstantially at a same phase during a heart beat cycle.

According to an aspect of some embodiments of the present invention,there is provided a computer software product, comprising acomputer-readable medium in which program instructions are stored, whichinstructions, when read by a computer, cause the computer to receive aplurality of 2D angiographic images of a portion of a vasculature andexecute the method for construction of a vascular tree model.

According to an aspect of some embodiments of the present invention,there is provided a system for vascular assessment comprising: acomputer logically connected to an angiographic imaging device forcapturing a plurality of 2-D images of a portion of vasculature of asubject, configured to: accept the plurality of 2-D angiographic imagesfrom the plurality of angiographic imaging devices; extract, from eachof the plurality of 2-D angiographic images, an image feature data setcomprising 2-D feature positions of the blood vessel segments; adjustthe 2-D feature positions to minimize relative position error in a 3-Dcoordinate system common to the feature positions; find correspondencesof the 2-D feature positions among the image feature data sets such that2-D feature positions projected from a common blood vessel segmentregion to different images are associated; and determine a 3-Drepresentation of the 2-D feature positions based on inspection of 3-Dprojections determined from the associated 2-D feature positions.

According to some embodiments of the invention, the image feature setwhich the system is configured to extract comprises a centerline dataset including 2-D centerline positions ordered along the blood vesselsegments.

According to some embodiments of the invention, the system is configuredto use the positions of elements of the centerline data set as the 2-Dfeature positions.

According to some embodiments of the invention, the system is configuredto adjust the 2-D feature positions based on registration of the 2-Dimages in 3-D space according to parameters which bring the 2-Dcenterline positions into closer correspondence among their 3-Dback-projections.

According to some embodiments of the invention, the measurements ofradial vascular width comprise distances between connected routesrunning along either side of the 2-D centerline positions, and theconnected routes comprise pixels imaging the boundary region of avascular wall.

According to some embodiments of the invention, the imagetransformation-based adjustment is iteratively performable for at leasta second selection of images for the first and second sets of images.

According to some embodiments of the invention, the portion of thevasculature comprises a tree of coronary arteries to at least a thirdbranch point from the main coronary artery.

According to an aspect of some embodiments of the present invention,there is provided a method of construction of a vascular tree modelcomprising: receiving 2-D images of a vascular tree, each associatedwith a corresponding image plane position; automatically identifyingvascular features of the 2-D images; identifying homologous vascularfeatures among the images by: geometrically projecting rays from thevascular features within the image plane positions, and passing througha common image target space, and associating features havingintersecting rays as homologous.

According to some embodiments of the invention, intersection of rayscomprises passing within a predefined distance from one another.

According to some embodiments of the invention, the image planepositions are iteratively updated to reduce error in ray intersections,and the identifying of homologous vascular features is repeatedthereafter.

According to an aspect of some embodiments of the present invention,there is provided a method of construction of a vascular tree modelcomprising iteratively back-projecting rays from features in a pluralityof 2-D images to a common 3-D space, determining errors in theintersections of rays from features common among the plurality of 2-Dimages, adjusting the 2-D images, and repeating the back-projecting,determining, and adjusting at least a first additional time.

According to an aspect of some embodiments of the present invention,there is provided a model of a portion of a vasculature, whereinelements of the model are associated with a plurality of locationdescriptions selected from among the group consisting of: the coordinatespace of a plurality of 2-D angiographic images, the coordinate space ofa common 3-D space, and a vascular graph space having 1-D extentsbranched from connected nodes.

According to an aspect of some embodiments of the present invention,there is provided a method for vascular assessment comprising: receivinga plurality of 2-D angiographic images of a portion of a vasculature ofa subject; producing, within 20 minutes of the receiving, and byautomatic processing of the images, a first 3-D vascular tree model overa portion of the vasculature comprising a stenotic heart artery; anddetermining automatically, based on the vascular tree model, an indexquantifying a capacity for restoration of flow by opening of a stenosis.

According to some embodiments of the invention, the indication of acapacity for restoration of flow by opening of a stenosis comprisescalculations based on change of a vascular width.

According to some embodiments of the invention, the automatic processingis performed within ten thousand trillion computational operations.

According to some embodiments of the invention, the automatic processingcomprises formation of a model which does not include a radiallydetailed 3-D representation of a vascular wall.

According to some embodiments of the invention, the determiningautomatically and the automatic processing comprise formation of a modelwhich does not include dynamic flow modeling.

According to some embodiments of the invention, the determiningautomatically comprises linear modeling of vascular flowcharacteristics.

According to some embodiments of the invention, the vascular tree modelrepresents vascular width as a function of vascular extent.

According to some embodiments of the invention, vascular extentcomprises distance along a vascular segment located at a nodal positionon the vascular tree model.

According to some embodiments of the invention, the first 3-D vasculartree model comprises at least 3 branch nodes between vascular segments.

According to some embodiments of the invention, the first 3-D vasculartree model comprises vascular centerlines and vascular widthstherealong.

According to some embodiments of the invention, the first 3-D vesseltree is produced within 5 minutes.

According to some embodiments of the invention, the method comprisescalculating an FFR characteristic for at least one vascular segment ofthe vessel tree.

According to some embodiments of the invention, calculating the FFRcharacteristic comprises producing a second vascular tree model based onthe first model, with a difference that vascular width is represented aslarger in the second model, and comparing the first and second vasculartree models.

According to some embodiments of the invention, the comparing comprisesobtaining a ratio of flow modeled in the first and second vascular treemodels for the at least one vascular segment.

According to some embodiments of the invention, the FFR characteristicis calculated within 1 minute of producing the first 3-D vascular treemodel.

According to some embodiments of the invention, the FFR characteristicis calculated within 10 seconds of producing the first and the second3-D vascular tree models.

According to some embodiments of the invention, the FFR characteristicis a predictor of a pressure measurement-determined FFR index with asensitivity of at least 95%.

According to some embodiments of the invention, the method comprisesproducing a projection of a portion of the first 3-D vessel tree into a2-D coordinate reference frame shared by at least one of the pluralityof 2-D angiographic images.

According to some embodiments of the invention, the at least one imageis transformed from an original coordinate reference frame into acoordinate reference frame which is defined relative to the 3-Dcoordinate reference frame of the 3-D vessel tree.

According to some embodiments of the invention, the subject isvascularly catheterized during imaging that produces the receivedplurality of 2-D angiographic images, and remains catheterized duringthe receiving of images, and producing of a first 3-D vascular treemodel.

According to some embodiments of the invention, the method comprises:imaging the subject to produce a second plurality of 2-D angiographicimages after a first producing of a first vascular tree model; a secondreceiving of images, the images comprising the second plurality ofimages; and a second producing of a first 3-D vascular tree model;wherein the subject remains vascularly catheterized.

According to some embodiments of the invention, the producing occursinteractively with an ongoing catheterization procedure of the subject.

According to some embodiments of the invention, the calculating of anFFR characteristic occurs interactively with an ongoing catheterizationprocedure of the subject.

According to an aspect of some embodiments of the present invention,there is provided a system for vascular assessment comprising: acomputer logically connected to an angiographic imaging device forcapturing a plurality of 2-D images of a portion of vasculature of asubject, and configured to calculate a vascular tree model therefromwithin 5 minutes; wherein an index of vascular function which indicatesa capacity for restoration of flow by opening of a stenosis isdeterminable based on the vascular tree model within another minute.

According to some embodiments of the invention, determination based onthe vascular tree model comprises generation of a second vascular treemodel derived from the vascular tree model by widening a modeledvascular width in the region of a stenosis.

In some embodiments of the invention, one or more models of a patient'svascular system are produced.

In some embodiments, a first model is produced from actual datacollected from images of the patient's vascular system. Optionally, theactual data includes a portion of the vascular system which includes atleast one blood vessel with stenosis. In these embodiments, the firstmodel describes a portion of the vasculature system which includes atleast one blood vessel with stenosis. This model is interchangeablyreferred to as a stenotic model. Optionally, the actual data includes aportion of the vascular system which includes at least one blood vesselwith stenosis and a crown. In these embodiments the stenotic model alsoincludes information pertaining to the shape and/or volume of the crown,and information pertaining to blood flow and/or resistance to blood flowin the crown.

In some embodiments the first model is used for calculating an indexindicative of vascular function. Preferably, the index is alsoindicative of potential effect of revascularization. For example, theindex can be calculated based on a volume of a crown in the model and ona contribution of a stenosed vessel to the resistance to blood flow inthe crown.

In some embodiments of the present invention a second model is producedfrom the actual data, changed so that one or more stenoses present inthe patient's vascular system are modeled as if they had beenrevascularized.

In some embodiments the first model and the second model are compared,and the index indicative of the potential effect of revascularization isproduced, based on comparing physical characteristics in the first modeland in the second model.

In some embodiments the index is a Fractional Flow Reserve (FFR), asknown in the art.

In some embodiments the index is some other measure which potentiallycorrelates to efficacy of performing revascularization of one or morevessels, optionally at locations of stenosis.

According to an aspect of some embodiments of the present inventionthere is provided a method for vascular assessment. The methodcomprises, receiving a plurality of 2D angiographic images of a portionof a vasculature of a subject; and using a computer for processing theimages and producing, within less than 60 minutes, a first vessel treeover a portion of the vasculature.

According to some embodiments of the invention the vasculature hastherein at least a catheter other than an angiographic catheter, andwherein the images are processed and the tree is produced while thecatheter is in the vasculature.

According to some embodiments of the invention the method comprisesusing the vascular model for calculating an index indicative of vascularfunction.

According to some embodiments of the invention the index is indicativeof the need for revascularization.

According to some embodiments of the invention the calculation is withinless than 60 minutes.

According to an aspect of some embodiments of the present inventionthere is provided a method of analyzing angiographic images. The methodcomprises: receiving a plurality of 2D angiographic images of a portionvasculature of a subject; and using a computer for processing the imagesto produce a tree model of the vasculature.

According to an aspect of some embodiments of the present inventionthere is provided a method of treating a vasculature. The methodcomprises: capturing a plurality of 2D angiographic images of a vascularsystem of a subject being immobilized on a treatment surface; and, whilethe subject remains immobilized: processing the images and producing avessel tree over the vascular system; identifying a constricted bloodvessel in the tree; and inflating a stent at a site of the vasculaturecorresponding to the constricted blood vessel in the tree.

According to some embodiments of the invention the plurality of 2Dangiographic images comprise at least three 2D angiographic images,wherein the tree model is a 3D tree model.

According to some embodiments of the invention the method comprisesidentifying in the first vessel tree a stenosed vessel and a crown ofthe stenosed vessel, and calculating a resistance to fluid flow in thecrown; wherein the index is calculated based on a volume of the crown,and on a contribution of the stenosed vessel to the resistance to fluidflow.

According to some embodiments of the invention the vessel tree comprisesdata pertaining to location, orientation and diameter of vessels at aplurality of points within the portion of the vasculature.

According to some embodiments of the invention the method comprisesprocessing the images to produce a second three-dimensional vessel treeover the vasculature, the second vessel tree corresponding to the firstvessel tree in which a stenotic vessel is replaced with an inflatedvessel; wherein the calculation of the index is based on the first treeand the second tree.

According to some embodiments of the invention the method comprisesprocessing the images to produce a second three-dimensional vessel treeover the vasculature, the second vessel tree corresponding to a portionof the vascular system which does not include a stenosis and which isgeometrically similar to the first vessel tree; wherein the calculationof the index is based on the first tree and the second tree.

According to some embodiments of the invention the method comprisesobtaining a Fractional Flow Ratio (FFR) based on the index.

According to some embodiments of the invention the method comprisesdetermining, based on the index, a ratio between maximal blood flow inan area of a stenosis and a maximal blood flow in a same area withoutstenosis.

According to some embodiments of the invention the method comprisesminimally invasively treating a stenosed vessel.

According to some embodiments of the invention the treatment is executedless than one hour from the calculation of the index.

According to some embodiments of the invention the method comprisesstoring the tree in a computer readable medium.

According to some embodiments of the invention the method comprisestransmitting the tree to a remote computer.

According to some embodiments of the invention the invention the methodcomprises capturing the 2D angiographic images.

According to some embodiments of the invention the capturing theplurality of 2D angiographic images is effected by a plurality ofimaging devices to capture the plurality of 2D angiographic images.

According to some embodiments of the invention the capturing theplurality of 2D angiographic images comprises synchronizing theplurality of imaging devices to capture the plurality of imagessubstantially at a same phase during a heart beat cycle.

According to some embodiments of the invention the synchronizing isaccording to the subject's ECG signal.

According to some embodiments of the invention the method comprises:detecting corresponding image features in each of N angiographic images,where N is an integer greater than 1; calculating image correctionparameters based on the corresponding image features; and based on thecorrection parameters, registering N−1 angiographic images togeometrically correspond to an angiographic image other than the N−1angiographic images.

According to some embodiments of the invention the method comprisesdefining a surface corresponding to a shape of the heart of the subject,and using the surface as a constraint for the detection of thecorresponding image features.

According to some embodiments of the invention the method comprisescompensating for breath and patient movement.

According to an aspect of some embodiments of the present inventionthere is provided a computer software product. The computer softwareproduct comprises a computer-readable medium in which programinstructions are stored, which instructions, when read by a computer,cause the computer to receive a plurality of 2D angiographic images of asubject's vascular system and execute the method as delineated above andoptionally as further detailed below.

According to an aspect of some embodiments of the present inventionthere is provided a system for vascular assessment. The systemcomprises: a plurality of imaging devices configured for capturing aplurality of 2D angiographic images of a vascular system of a subject;and a computer configured for receiving the plurality of 2D images andexecuting the method the method as delineated above and optionally asfurther detailed below.

According to an aspect of some embodiments of the present inventionthere is provided a system for vascular assessment comprising: acomputer functionally connected to a plurality of angiographic imagingdevices for capturing a plurality of 2D images of a portion ofvasculature of a subject, configured to: accept data from the pluralityof angiographic imaging devices; and process the images to produce atree model of the vasculature, wherein the tree model comprisesgeometric measurements of the vasculature at one or more locations alonga vessel of at least one branch of the vasculature.

According to some embodiments of the invention the system comprises asynchronization unit configured to provide the plurality of angiographicimaging devices with a synchronization signal for synchronizing thecapturing of the plurality of 2D images of the vasculature.

According to some embodiments of the invention the computer isconfigured to accept a subject ECG signal, and to select, based on theECG signal, 2D images corresponding to substantially a same phase duringa heart beat cycle.

According to some embodiments of the invention the system comprises animage registration unit configured for: detecting corresponding imagefeatures in each of N angiographic images, where N is an integer greaterthan 1; calculating image correction parameters based on thecorresponding image features; and based on the correction parameters,registering N−1 angiographic images to geometrically correspond to anangiographic image other than the N−1 angiographic images.

According to some embodiments of the invention the computer isconfigured for defining a surface corresponding to a shape of the heartof the subject, and using the surface as a constraint for the detectionof the corresponding image features.

According to some embodiments of the invention the computer isconfigured for compensating for breath and patient movement.

According to some embodiments of the invention the compensatingcomprises iteratively repeating the detection of the corresponding imagefeatures each time for a different subset of angiographic images, andupdating the image correction parameters responsively to the repeateddetection of the corresponding image features.

According to some embodiments of the invention N is greater than 2.According to some embodiments of the invention N is greater than 3.

According to some embodiments of the invention the corresponding imagefeatures comprise at least one of a group consisting of an origin of thetree model, a location of minimal radius in a stenosed vessel, and abifurcation of a vessel.

According to some embodiments of the invention the tree model comprisesdata pertaining to location, orientation and diameter of vessels at aplurality of points within the portion of the vasculature.

According to some embodiments of the invention, the tree model comprisesmeasurements of the vasculature at one or more locations along at leastone branch of the vasculature.

According to some embodiments of the invention the geometricmeasurements of the vasculature are at one or more locations along acenterline of at least one branch of the vasculature.

According to some embodiments of the invention the tree model comprisesdata pertaining to blood flow characteristics in at one or more of theplurality of points.

According to some embodiments of the invention the portion of thevasculature comprises the heart arteries.

According to an aspect of some embodiments of the present inventionthere is provided a method for vascular assessment comprising: receivinga plurality of 2D angiographic images of a portion of a vasculature of asubject, and processing the images to produce a stenotic model over thevasculature, the stenotic model having measurements of the vasculatureat one or more locations along vessels of the vasculature; obtaining aflow characteristic of the stenotic model; and calculating an indexindicative of vascular function, based, at least in part, on the flowcharacteristic in the stenotic model.

According to some embodiments of the invention the flow characteristicof the stenotic model comprises resistance to fluid flow.

According to some embodiments of the invention the invention the methodcomprises identifying in the first stenotic model a stenosed vessel anda crown of the stenosed vessel, and calculating the resistance to fluidflow in the crown; wherein the index is calculated based on a volume ofthe crown, and on a contribution of the stenosed vessel to theresistance to fluid flow.

According to some embodiments of the invention the flow characteristicof the stenotic model comprises fluid flow.

According to some embodiments of the invention the stenotic model is athree-dimensional vessel tree.

According to some embodiments of the invention the vessel tree comprisesdata pertaining to location, orientation and diameter of vessels at aplurality of points within the portion of the vasculature.

According to some embodiments of the invention the processing comprises:extending the stenotic model by one bifurcation; calculating a new flowcharacteristic in the extended stenotic model; updating the indexresponsively to the new flow characteristic and according to apredetermined criterion; and iteratively repeating the extending, thecalculating and the updating.

According to some embodiments of the invention the method comprisesprocessing the images to produce a second model over the vasculature,and obtaining a flow characteristic of the second model; wherein thecalculation of the index is based on the flow characteristic in thestenotic model and on the flow characteristic in the second model.

According to some embodiments of the invention the method the secondmodel is a normal model, comprising an inflated vessel replacing astenotic vessel in the stenotic model.

According to some embodiments of the invention the stenotic model is athree-dimensional vessel tree and the second model is a secondthree-dimensional vessel tree.

According to some embodiments of the invention each of the modelscorresponds to a portion of the vasculature which is between twoconsecutive bifurcations of the vasculature and which includes astenosis.

According to some embodiments of the invention each of the modelscorresponds to a portion of the vasculature which includes a bifurcationof the vasculature.

According to some embodiments of the invention each of the modelscorresponds to a portion of the vasculature which includes a stenosisand which extends at least one bifurcation of the vasculature beyond thestenosis.

According to some embodiments of the invention the each of the modelscorresponds to a portion of the vasculature which includes a stenosisand which extends at least three bifurcations of the vasculature beyondthe stenosis.

According to some embodiments of the invention the method wherein eachof the models corresponds to a portion of the vasculature which includesa stenosis and which extends distally as far as resolution of the imagesallows.

According to some embodiments of the invention the stenotic modelcorresponds to a portion of the vasculature which includes a stenosis,and the second model corresponds to a portion of the vasculature whichdoes not include a stenosis and which is geometrically similar to thestenotic model.

According to some embodiments of the invention the processing comprises:extending each of the models by one bifurcation; calculating a new flowcharacteristic in each extended model; updating the index responsivelyto the new flow characteristics and according to a predeterminedcriterion; and iteratively repeating the extending, the calculating andthe updating.

According to some embodiments of the invention the index is calculatedbased on a ratio of the flow characteristic in the stenotic model to theflow characteristic in the second model.

According to some embodiments of the invention the index is indicativeof the need for revascularization.

According to an aspect of some embodiments of the present inventionthere is provided a method for vascular assessment including producing astenotic model of a subject's vascular system, the stenotic modelincluding measurements of the subject's vascular system at one or morelocations along a vessel centerline of the subject's vascular system,obtaining a flow characteristic of the stenotic model, producing asecond model, of a similar extent of the subject's vascular system asthe stenotic model, obtaining the flow characteristic of the secondmodel, and calculating an index indicative of the need forrevascularization, based on the flow characteristic in the stenoticmodel and on the flow characteristic in the second model.

According to some embodiments of the invention, the second model is anormal model, including an inflated vessel replacing a stenotic vesselin the stenotic model.

According to some embodiments of the invention, the vascular systemincludes the subject's heart arteries.

According to some embodiments of the invention, the producing a stenoticmodel of a subject's vascular system includes using a plurality ofangiographic imaging devices for capturing a plurality of 2D images ofthe subject's vascular system, and producing the stenotic model based onthe plurality of 2D images.

According to some embodiments of the invention, the flow characteristicincludes fluid flow.

According to some embodiments of the invention, the obtaining the flowcharacteristic of the stenotic model includes measuring fluid flow inthe subject's vascular system at the one or more locations in the extentof the subject's vascular system included in the stenotic model, and theobtaining the flow characteristic of the second model includescalculating fluid flow in the subject's vascular system at the one ormore locations in the extent of the subject's vascular system includedin the second model, based, at least in part, on correcting the fluidflow of the stenotic model to account for an inflated vessel.

According to some embodiments of the invention, the flow characteristicincludes resistance to fluid flow.

According to some embodiments of the invention, the obtaining the flowcharacteristic of the stenotic model includes calculating a resistanceto flow based, at least in part, on the subject vascular system crosssectional area at the one or more locations in the extent of thesubject's vascular system included in the stenotic model, and obtainingthe flow characteristic of the second model includes calculating theresistance to flow based, at least in part, on the subject vascularsystem inflated cross sectional area at the one or more locations in theextent of the subject's vascular system included in the second model.

According to some embodiments of the invention, the extent of each oneof the stenotic model and the second model includes a segment of thevascular system, between two consecutive bifurcations of the vascularsystem, which includes a stenosis.

According to some embodiments of the invention, the extent of each oneof the stenotic model and the second model includes a segment of thevascular system which includes a bifurcation of the vascular system.

According to some embodiments of the invention, each one of the stenoticmodel and the second model include an extent of the vascular systemwhich includes a stenosis and extends at least one bifurcation of thevascular system beyond the stenosis.

According to some embodiments of the invention, each one of the stenoticmodel and the second model include an extent of the vascular systemwhich includes a stenosis and an inflated stenosis respectively andextends at least three bifurcations of the vascular system beyond thestenosis.

According to some embodiments of the invention, each one of the stenoticmodel and the second model include an extent of the vascular systemwhich includes a stenosis and extends distally as far as resolution ofan imaging modality allows.

According to some embodiments of the invention, each one of the stenoticmodel and the second model include an extent of the vascular systemwhich includes a stenosis and extends distally at least one bifurcationof the vascular system beyond the stenosis, and further includingstoring the flow characteristic of the stenotic model as a previous flowcharacteristic of the stenotic model and storing the flow characteristicof the second model as a previous flow characteristic of the secondmodel, extending the extent of the stenotic model and the second modelby one more bifurcation, calculating a new flow characteristic in thestenotic model and calculating a new flow characteristic in the secondmodel, deciding whether to calculate the index indicative of the needfor revascularization as follows: if the new flow characteristic of thestenotic model differs from the previous characteristic of the stenoticmodel by less than a first specific difference, and the new flowcharacteristic of the second model differs from the previouscharacteristic of the second model by less than a second specificdifference, then calculating the index indicative of the need forrevascularization, else repeating the storing, the extending, thecalculating, and the deciding.

According to some embodiments of the invention, the stenotic modelincludes an extent of the vascular system which includes a stenosis, thesecond model includes an extent of the vascular system which does notinclude a stenosis and which is geometrically similar to the firstmodel.

According to some embodiments of the invention, the index is calculatedas a ratio of the flow characteristic in the stenotic model to the flowcharacteristic in the second model.

According to some embodiments of the invention, the calculated index isused to determine a Fractional Flow Ratio (FFR).

According to some embodiments of the invention, the calculated index isused to determine a ratio between maximal blood flow in an area ofstenosis and a maximal blood flow in a same area without stenosis.

According to some embodiments of the invention, the producing a stenoticmodel, the obtaining a flow characteristic of the stenotic model, theproducing a second model, the obtaining the flow characteristic of thesecond model, and the calculating an index, are all performed during adiagnostic catheterization, before a catheter used for the diagnosticcatheterization is withdrawn from the subject's body.

According to an aspect of some embodiments of the present inventionthere is provided a method for vascular assessment including capturing aplurality of 2D angiographic images of a subject's vascular system,producing a tree model of the subject's vascular system, the tree modelincluding geometric measurements of the subject's vascular system at oneor more locations along a vessel centerline of at least one branch ofthe subject's vascular system, using at least some of the plurality ofcaptured 2D angiographic images, and producing a model of a flowcharacteristic of the first tree model.

According to some embodiments of the invention, the vascular systemincludes the subject's heart arteries.

According to some embodiments of the invention, the capturing aplurality of 2D angiographic images includes using a plurality ofimaging devices to capture the plurality of 2D angiographic images.

According to some embodiments of the invention, the capturing aplurality of 2D angiographic images includes synchronizing the pluralityof imaging devices to capture the plurality of images at a same moment.

According to some embodiments of the invention, the synchronizing usesthe subject's ECG signal.

According to some embodiments of the invention, the synchronizingincludes detecting corresponding image features in at least a first 2Dangiographic image and a second 2D angiographic image of the pluralityof 2D angiographic images, calculating image correction parameters basedon the corresponding image features, and registering at least the second2D angiographic image to geometrically correspond to the first 2Dangiographic image, wherein the corresponding image features include atleast one of a group consisting of an origin of the tree model, alocation of minimal radius in a stenosed vessel, and a bifurcation of avessel.

According to an aspect of some embodiments of the present inventionthere is provided a system for vascular assessment including a computerfunctionally connected to a plurality of angiographic imaging devicesfor capturing a plurality of 2D images of a patient's vascular system,configured to accept data from the plurality of angiographic imagingdevices, produce a tree model of the subject's vascular system, whereinthe tree model includes geometric measurements of the subject's vascularsystem at one or more locations along a vessel centerline of at leastone branch of the subject's vascular system, using at least some of theplurality of captured 2D images, and produce a model of flowcharacteristics of the tree model.

According to some embodiments of the invention, the vascular systemincludes the subject's heart arteries.

According to some embodiments of the invention, further including asynchronization unit configured to provide the plurality of angiographicimaging devices with a synchronization signal for synchronizing thecapturing of the plurality of 2D images of the subject's vascularsystem.

According to some embodiments of the invention, further including asynchronization unit configured to accept a subject ECG signal, and toselect 2D images from the data from the plurality of angiographicimaging devices at a same cardiac phase in the 2D images.

According to some embodiments of the invention, further including animage registration unit configured to detect corresponding imagefeatures in at least a first 2D image and a second 2D image from thedata from the plurality of angiographic imaging devices, to calculateimage correction parameters based on the corresponding image features,and to register at least the second 2D image to geometrically correspondto the first 2D image, wherein the corresponding image features includeat least one of a group consisting of an origin of the tree model, alocation of minimal radius in a stenosed vessel, and a bifurcation of avessel.

According to an aspect of some embodiments of the present inventionthere is provided a method for vascular assessment including producing astenotic model of a subject's vascular system, the stenotic modelincluding geometric measurements of the subject's vascular system at oneor more locations along a vessel centerline of the subject's vascularsystem, including an extent of the vascular system which includes astenosis and extends at least one bifurcation of the vascular systembeyond the stenosis, obtaining a flow characteristic of the stenoticmodel, producing a second model, of a similar extent of the subject'svascular system as the stenotic model, obtaining the flow characteristicof the second model, and calculating an index indicative of the need forrevascularization, based on the flow characteristic in the stenoticmodel and on the flow characteristic in the second model, and furtherincluding storing the flow characteristic of the stenotic model as aprevious flow characteristic of the stenotic model and storing the flowcharacteristic of the second model as a previous flow characteristic ofthe second model, extending the extent of the stenotic model and thesecond model by one more bifurcation, calculating a new flowcharacteristic in the stenotic model and calculating a new flowcharacteristic in the second model, deciding whether to calculate theindex indicative of the need for revascularization as follows: if thenew flow characteristic of the stenotic model differs from the previouscharacteristic of the stenotic model by less than a first specificdifference, and the new flow characteristic of the second model differsfrom the previous characteristic of the second model by less than asecond specific difference, then calculating the index indicative of theneed for revascularization, else repeating the storing, the extending,the calculating, and the deciding.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.Implementation of the method and/or system of embodiments of theinvention can involve performing or completing selected tasks manually,automatically, or a combination thereof.

For example, hardware for performing selected tasks according toembodiments of the invention could be implemented as a chip or acircuit. As software, selected tasks according to embodiments of theinvention could be implemented as a plurality of software instructionsbeing executed by a computer using any suitable operating system. In anexemplary embodiment of the invention, one or more tasks according toexemplary embodiments of method and/or system as described herein areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings and images.With specific reference now to the drawings and images in detail, it isstressed that the particulars shown are by way of example and forpurposes of illustrative discussion of embodiments of the invention. Inthis regard, the description taken with the drawings and images makesapparent to those skilled in the art how embodiments of the inventionmay be practiced.

In the drawings:

FIG. 1 depicts an original image and a Frangi-filter processed image,processed according to some exemplary embodiments of the invention;

FIG. 2 depicts a light-colored center line overlaid on top of theoriginal image of FIG. 1 , according to some exemplary embodiments ofthe invention;

FIG. 3A is an image of a coronary vessel tree model, produced accordingto some exemplary embodiments of the invention;

FIG. 3B is an image of a coronary vessel tree model of FIG. 3A, withtree branch tags added according to some exemplary embodiments of theinvention;

FIG. 3C is a simplified illustration of a tree model of a coronaryvessel tree, produced according to some exemplary embodiments of theinvention;

FIG. 4 is a set of nine graphical illustrations of vessel segment radiiproduced according to an example embodiment of the invention, along thebranches of the coronary vessel tree model depicted in FIG. 3C, as afunction of distance along each branch, according to some exemplaryembodiments of the invention;

FIG. 5 depicts a coronary tree model, a combination matrix depictingtree branch tags, and a combination matrix depicting tree branchresistances, all produced according to some exemplary embodiments of theinvention;

FIG. 6 depicts a tree model of a vascular system, with tags numberingoutlets of the tree model, produced according to an example embodimentof the invention, the tags corresponding to stream lines, according tosome exemplary embodiments of the invention;

FIG. 7 is a simplified illustration of a vascular tree model producedaccording to an example embodiment of the invention, including branchresistances R_(i) at each branch and a calculated flow Q_(i) at eachstream line outlet, according to some exemplary embodiments of theinvention;

FIG. 8 is a simplified flow chart illustration of FFR index generation,according to some exemplary embodiments of the invention;

FIG. 9 is a simplified flow chart illustration of another method of FFRindex generation, according to some exemplary embodiments of theinvention;

FIG. 10 is a simplified flow chart illustration of yet another method ofFFR index generation, according to some exemplary embodiments of theinvention;

FIG. 11 is a simplified drawing of vasculature which includes a stenosedvessel and a non-stenosed vessel, as related to according to someexemplary embodiments of the invention;

FIG. 12A is a simplified illustration of a hardware implementation of asystem for vascular assessment, constructed according to some exemplaryembodiments of the invention;

FIG. 12B is a simplified illustration of another hardware implementationof a system for vascular assessment constructed according to someexemplary embodiments of the invention;

FIG. 13 is a flow chart describing an exemplary overview of stages invascular model construction, according to some exemplary embodiments ofthe invention;

FIG. 14 is a flow chart describing an exemplary overview of details ofstages in vascular model construction, according to some exemplaryembodiments of the invention;

FIG. 15 depicts a schematic of an exemplary arrangement of imagingcoordinates for an imaging system, according to some exemplaryembodiments of the invention;

FIG. 16 is a simplified flowchart of processing operations comprised inanisotropic diffusion, according to some exemplary embodiments of theinvention;

FIG. 17A is a simplified flowchart of processing operations comprised inmotion compensation, according to some exemplary embodiments of theinvention;

FIG. 17B is a simplified flowchart of processing operations comprised inan alternative or additional method of motion compensation, according tosome exemplary embodiments of the invention;

FIGS. 18A-18B illustrate aspects of the calculation of a “cardiac shell”constraint for discarding bad ray intersections from calculatedcorrespondences among images, according to some exemplary embodiments ofthe invention;

FIG. 18C is a simplified flowchart of processing operations comprised inconstraining pixel correspondences to within a volume near the heartsurface, according to some exemplary embodiments of the invention;

FIGS. 19A-19D illustrate identification of homology among vascularbranches, according to some exemplary embodiments of the invention;

FIG. 19E is a simplified flowchart of processing operations comprised inidentifying homologous regions along vascular branches, according tosome exemplary embodiments of the invention;

FIG. 20A is a simplified flowchart of processing operations comprised inselecting a projection pair along a vascular centerline, according tosome exemplary embodiments of the invention;

FIG. 20B is a schematic representation of epipolar determination of 3-Dtarget locations from 2-D image locations and their geometricalrelationships in space, according to some exemplary embodiments of theinvention;

FIG. 21 is a simplified flowchart of processing operations comprised ingenerating an edge graph, and in finding connected routes along an edgegraph, according to some exemplary embodiments of the invention;

FIG. 22 is a simplified schematic of an automatic VSST scoring system,according to some exemplary embodiments of the invention;

FIG. 23 shows an exemplary branching structure having recombiningbranches, according to some exemplary embodiments of the invention; and

FIG. 24 is a Bland-Altman plot of the difference of FFR index andimage-based FFR index as a function of their average, according to someexemplary embodiments of the invention.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to vascularmodeling, and, more particularly, but not exclusively, to the use of avascular model for producing indices relating to vascular function anddiagnosis in real time—for example, during a catheterized imagingprocedure.

A broad aspect of some embodiments of the current invention relates tocalculation of a fractional flow reserve (FFR) index based on an imagingof a portion of a vascular system.

An aspect of some embodiments of the invention relates to calculation ofa model of vascular flow in a subject. In some embodiments, the portionof the vascular system which is imaged is coronary vasculature. In someembodiments, the vasculature is arterial. In some embodiments, vascularflow is modeled based on vascular diameter in 3-D reconstruction of avascular tree. Optionally, vascular resistance is determined based onvascular diameter. Optionally, vascular resistance is calculated for astenotic vessel, and for its vascular crown (vessels downstream of thestenotic vessel). In some embodiments, the FFR is calculated from ageneral 3-D reconstruction of a vascular tree, for example, of avascular tree reconstruction from a CT scan. In some embodiments,reconstruction is performed de novo, for example, from 2-D angiographicimage data. Optionally, a provided vascular tree is suited to thespecific requirements of FFR calculation, for example, by reduction to agraph representation of vascular width as a function of vascular extent.

An aspect of some embodiments of the invention relates to calculation ofFFR based on differences in flow between a vascular model of apotentially stenotic vasculature, and a different vascular model derivedfrom and/or homologous to the stenotic vasculature model. In someembodiments, changes to create an astenotic version of the vascularmodel comprise determinations of wall opening (widening) in stenoticregions based on reference width measurements obtained in one or moreother portions of the vasculature. In some embodiments, reference widthmeasurements are obtained from vascular portions on either side of astenosis. In some embodiments, reference width measurements are obtainedfrom a naturally astenotic vessel at a similar branch order to astenotic segment.

In some embodiments of the invention, the FFR index comprises a ratio offlow in model comprising a potentially stenotic vascular segment to amodel wherein said segment is replaced by a lower flow-resistancesegment, and/or the resistance to flow due to said segment is removed. Apotential advantage of this ratio determination is that the indexcomprises an expression of the effect of a potential therapeutictreatment to a vasculature, for example, an opening of a vascular regionby percutaneous coronary intervention (PCI) such as stent implantation.Another potential advantage of this ratio is that it measures aparameter (fractional flow reserve) which, though well-accepted asproviding an indication of a need for revascularization, is commonlydetermined in the art by invasive pressure measurements requiring directaccess to both sides of stenotic lesion.

A broad aspect of some embodiments of the current invention relates togeneration of a vascular tree model.

An aspect of some embodiments of the invention relates to constructionof a tree model of a portion of a mammalian vasculature, based onautomatic matching of features homologous among multiple vascularimages. In some embodiments of the invention, the tree model comprisesvascular segment centerlines. Optionally, the homologous matching isamong vascular segment centerlines and/or portions thereof. In someembodiments, the modeled spatial relationship among vascular segmentcenterlines comprises association of segment ends at branch nodes.

A potential advantage of using vascular segment centerlines, and/orother features readily identifiable from vascular segment data in animage, is that they provide a rich “metafeature” which can be subjectedto ray intersection tests by back-projecting rays from pairs (or alarger plurality) of individual images into a 3-D space based on theimaging configuration. In some embodiments, precise ray intersectionsare unnecessary; intersections within a volume are sufficient toestablish homology. While isolated features are potentially useful forsuch intersection-based homology identifications, it should be notedthat the extended character of paths along vascular segments (forexample) allows the use, in some embodiments, of correlation and/orconstraint techniques for further refinement of initial, potentiallytentative homology identifications. Ray intersections thus, in someembodiments, take the place of manual identification of homologousfeatures in different vascular projections.

In some embodiments of the invention, the modeled vasculature comprisesvasculature of a heart (cardiac vasculature), and specifically, of acoronary artery and its branches. In some embodiments, the tree modelcomprises 3-D position information for the cardiac vasculature.

An aspect of some embodiments of the invention relates to position (and,in particular, 3-D spatial position) in a model of cardiac vasculaturebeing based on and/or derived through coordinates defined by features ofthe vasculature itself. In some embodiments, the same vascular features(for example, vascular centerlines) both define the 3-D space of themodel, and additionally comprise the backbone of the model itself.Optionally, centerlines are represented according to both their 3-Dpositions in space, and as positions in a graph space defined by to avascular tree comprising connected segments of centerlines at nodes. Insome embodiments, a vascular segment comprises data associated with ablood vessel path (for example, a vascular centerline) connecting twobranch nodes.

In some embodiments of the invention, a “consensus” 3-D space defined bymatching among vascular feature positions is a result of tree modelconstruction.

A potential advantage of this vascular-centered modeling approachrelates to the heart (to which the vasculature is mechanically coupled)being in continual motion. In some embodiments, a vasculature model isbuilt up from a series of 2-D images taken sequentially. During a periodof cardiovascular imaging and/or among imaging positions, regions of thevasculature potentially change their actual and/or calibrated positions(absolute and/or relative) in 3-D space. This is due, for example, tothe beating of the heart, respiration, voluntary movements, and/ormisalignments in determining image projection planes. In someembodiments, an imaging protocol is modified to correct for thesemotions to an extent, for example, by synchronizing the moment ofimaging to a particular phase of the cardiac cycle (end of diastole, forexample). Nevertheless, errors potentially remain even after such afterthis, due, for example, to the natural variability of the cardiac cycle,effects of different physiological cycles (such as heart andrespiration) being out of phase, and limitations in the period forimaging available. Thus, potentially, there is no “natural” 3-D spacecommon to the raw 2-D image data. Targeting a consensus spacepotentially allows reframing the modeling problem in terms ofconsistency of modeling results.

While 3-D position changes, other features of vascular position, forexample, connectivity, and/or ordering of regions along the vasculature,are invariant with respect to motion artifacts. It is a potentialadvantage, accordingly, to use features of the vasculature itself todetermine a frame of reference within which a 3-D reconstruction can beestablished. In some embodiments, features on which 3-D reconstructionis based comprise 2-D centerlines of vessel segments present in aplurality of images, among which homologies are established byautomatic, optionally iterative methods. A potential advantage of usingcenterlines as the basis of 3-D modeling is that the centerlines whichanchor building the model tree are also useful as 1-D coordinate systemsin their own right. Thus, using centerlines as the reconstruction basishelps assure consistency and/or continuity of tree model featuresassociated with centerline position.

In some embodiments, other features related to the vasculature are usedas landmarks, for example, points of minimal vascular width, vascularbranch points, and/or vascular origin. Optionally, vascular featuressuch as centerlines are transformed alongside transformations to thelandmark features (without themselves being the target of cross-imagematching), before being integrated into the vascular tree model.

An aspect of some embodiments of the invention relates to the use ofiterative projections and back-projections between 2-D and 3-Dcoordinate systems to arrive at a consensus coordinate system whichrelates 2-D image planes to a 3-D system of target coordinates.

In some embodiments of the invention, assignment of consensus 3-Dpositions to landmark vascular features (for example, vascularcenterlines) projected during imaging from a single target region to aplurality of 2-D images comprises re-projection and/or re-registrationof the 2-D images themselves, the better to match a “consensus” 3-Dspace. Optionally, re-projection assigns to a 2-D image an image planewhich is different from the one originally recorded for it. Optionally,re-registration comprises non-linear distortions of the image, forexample, to compensate for deformations of the heart during imaging.Optionally, the re-projection and/or re-registration are performediteratively, for example, by defining different image groups as “target”and “matching” on different feature registration iterations. In someembodiments of the invention, a different number of images is used fordefining homologous features, and for subsequent analysis of additionalimage features (such as vascular width) to which said homologousfeatures are related.

An aspect of some embodiments of the invention relates to reductions inthe calculation complexity of tree determination, allowing more rapidprocessing to reach clinical conclusions.

In some embodiments, a portion of the image data (for example,“non-feature” pixel values) are optionally maintained in 2-Drepresentations, without a requirement for full 3-D reconstruction. Insome embodiments, for example, calculation of the 3-D positions ofnon-landmark features such as vascular wall positions, is therebyavoided, simplified, and/or postponed. In particular, in someembodiments, vascular edges are recognized from direct processing of 2-Dimage data (for example, comprising inspection of image gradientsperpendicular to vascular centerlines). Optionally, determined edges areprojected into 3-D space (represented, for example, as one or more radiiextending perpendicularly from 3-D centerline positions), without arequirement to project original image pixel data into 3-D voxelrepresentations.

Additionally or alternatively, vascular wall positions are determinedand/or processed (for example, to determine vascular resistance) withinone or more “1-D” spaces defined by a frame of reference comprisingposition along a centerline. Optionally, this processing is independent,for example, of any projection of wall position into a 3-D space. Insome embodiments, reduction of the model to a 1-D function of centerlineposition reduces a complexity of further calculation, for example, todetermine a vascular flow characteristic.

An aspect of some embodiments of the invention relates to relationshipsamong vascular model components having different dimensionality. In someembodiments, 1-D, 2-D and/or 3-D positions, and/or logical connectivity,and/or characteristics comprising non-positional or partiallynon-positional properties are related to one another by directfunctions, and/or indirectly through intermediate frames of reference.

In some embodiments, for example, a vascular model comprises one or moreof the following features:

-   -   2-D images having positions in a 3-D space defined by        relationships among homologous features therein;    -   Vascular extents comprising one or more 1-D axes for functions        of one or more vascular characteristics, for example diameter,        radius, flow, flow resistance, and/or curvature;    -   Vascular extents comprising one or more 1-D axes for functions        of position in 3-D space;    -   Connectivity among vascular extents, described as nodes relative        to positions along vascular extents (for example, nodes        connecting the ends of vascular segments);    -   2-D images into which 1-D axes of vascular extents are mapped;    -   2-D frames comprising vascular extent along one axis, and image        data orthogonal to the vascular extent along a second axis.

A broad aspect of some embodiments of the current invention relates toreal-time determination of a vascular tree model and/or use thereof toprovide clinical diagnostic information during a period when acatheterization procedure for a subject is underway.

An aspect of some embodiments of the invention relates to takingadvantage of real-time automatic vascular state determination tointeract with a clinical procedure as it is underway. Real-timedetermination, in some embodiments, comprises determination within thetime-frame of a catheterization procedure, for example 30 minutes, anhour, or a lesser, greater, or intermediate time. More particularly,real-time determination comprises a determination which is timely foraffecting decisions and/or outcomes of a catheterization procedure,which begins with the images that vascular state determination is basedon. For example, it is a potential advantage to select a particularportion of a vascular tree for initial calculations, where it is likelythat the calculation will be completed in a sufficiently short time toaffect a decision to perform a particular PCI procedure, such asimplantation of a stent. For example, a 5 minute delay for calculationof FFR comprising two main vascular branches can, when a first branchappears to be of particular interest based on a cursory review of imagedata, potentially be reduced to a 2.5 minute delay, by selecting thefirst branch to be the initial target of computations. Additionally oralternatively, rapid calculation allows FFR results to be updated one ormore times during the course of a catheterization procedure. Forexample, a first stent implantation potentially changes perfusion stateat other sites sufficiently to induce autoregulatory changes in vascularwidth, which in turn could change the expected impact of a subsequentstent implantation. Also for example, the imaged effects of an actualstent implantation on vascular width can be compared to predictedeffects, in order to verify that a desired effect on flow capacity hasbeen achieved. In some embodiments of the invention, provision is madeto allow interface control of how a vascular model and/or a vascularcharacteristic is calculated, to control model updates based on newlyavailable image data, and/or to select comparison among real and/orpredicted vascular state models.

An aspect of some embodiments of the invention relates to building of avascular tree model which is suited to targeted prediction of theresults of a potential clinical intervention. Optionally, the clinicalintervention is a PCI procedure such as implantation of a stent. In someembodiments of the invention, targeting comprises focusing stages ofvascular tree model construction such that they lead directly to abefore/after result in terms of a vascular parameter which is availablefor clinical modification. In some embodiments, the vascular parameteris vascular width (modifiable, for example, by stent implantation). Apotential advantage of focusing modeling on determining a differencebetween before- and after-treatment states of a vasculature is that theeffects of model simplifications due to approximations of other vasculardetails potentially cancel out (and/or are reduced in magnitude). Inparticular, they are potentially reduced in importance relative to anoperational concern such as: “will the change created by an interventionusefully improve the clinical situation in terms of the known effects ofthe variable which the intervention targets?” As one potential result,calculations which might otherwise be performed to fully modelfunctional and/or anatomical properties of a vasculature can be omitted.Potentially, this increases the speed with which a flow index can beproduced.

An aspect of some embodiments of the invention relates to theformulation of a model representation of a vasculature targets provisionof a framework for structuring one or more selected, clinically relevantparameters (such as vascular width, flow resistance and flow itself). Insome embodiments, the structure comprises a vascular-extent approach tomodeling, in which position along blood vessel segments provides theframe of reference. Optionally, the vascular-extent frame of referencecomprises a division among node-linked branches of a vascular tree.Potentially, this comprises a reduction in dimensionality which savescalculation time.

In some embodiments, a model of 3-D positions in a vascular model isformed from potentially incomplete or inaccurate initial positionalinformation. This is achieved, for example, by annealing to aself-consistent framework by an iterative process of adjusting thepositional information to achieve greater consistency among the acquireddata. Adjusting comprises, for example, operations such as transformingimage planes, deforming images themselves to achieve greater similarity,and/or discarding outliers which interfere with determination of aconsensus. Potentially, an alternative approach which seeks to ensurefidelity of the framework to a particular real-world configuration (forexample, the “real” 3-D configuration or configurations of a portion ofa vasculature in space) is computationally expensive relative to thebenefit gained for estimation of targeted parameters. In contrast, aframework for which the emphasis is placed on internal consistency inthe service of supporting calculations relating to a target parametercan make use of a consensus-like approach to potentially reducecomputational load. In particular, this approach is potentially wellsuited to be combined with a calculation of a change in a vascularsystem, as described hereinabove.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings. The invention is capable of otherembodiments or of being practiced or carried out in various ways.

It is noted that in example embodiments described below, the coronaryvessel system, and more specifically the coronary artery system, isused. The example is not meant to limit embodiments of the invention tothe coronary arteries, as embodiments of the invention potentially applyto other vessel systems, such as, for example, the vein system and thelymph system.

In some embodiments, a first model of vascular flow in a subject, basedon imaging the subject's vascular system, is constructed. Typically, thefirst model is constructed of a vascular system which includes a problemsection of the vascular system, such as a stenosis in at least part of avessel. In some embodiments of the present invention, the first modelcorresponds to a portion of the vascular system which includes at leastone blood vessel with stenosis. In these embodiments, the first modeldescribes a portion of the vasculature system which includes at leastone blood vessel with stenosis and a crown. In these embodiments thefirst model optionally includes information pertaining to the shapeand/or volume of the crown, and information pertaining to blood flowand/or resistance to blood flow in the stenosed blood vessel and/or thecrown.

Typically, but not necessarily, a second model is constructed. Thesecond model optionally describes an at least partially healthiervascular system corresponding to the first model. In some embodimentsthe second model is constructed by changing a stenosis in the firstmodel to be more open, as it would be if a stent were to open thestenosis; and in some embodiments the second model is constructed bychoosing a section of the subject's vascular system which includes ahealthy vessel similar to the problem vessel of the first model, andusing it to replace a stenosed vessel.

Vascular model construction is described hereinbelow.

In some embodiments, an index indicative of the need forrevascularization is calculated. This can be done based on the firstmodel or based on a comparison between the first and the second modelsof the vascular flow. The index is optionally used similarly to thepressure measurement-derived FFR index, to assess whether a stenosedvessel affects flow in the vascular system to such an extent that theprognosis for improvement in the subject's condition following inflationof the stenosed vessel is higher than the likelihood for complicationsresulting from the inflation itself.

The terms “FFR” and “FFR index” in all their grammatical forms are usedthroughout the present specification and claims to stand for theabove-mentioned index, and not only for the FFR index mentioned in theBackground section as an invasive measurement involving insertion of aguidewire equipped with a miniature pressure transducer across thestenosis. In some instances—in particular where distinctions amongspecific types of FFR and FFR-like indices are discussed—a subscript isused to distinguish them, for example FFR_(pressure) for FFR derivedfrom pressure measurements, and/or FFR_(flow) where FFR is expressed interms of flow determinations.

Acquiring Data for Constructing a Vascular Model

In some embodiments, data for modeling a vascular system comprisesmedical imaging data.

In some embodiments of the invention, the data is from minimallyinvasive angiographic images, for example, X-ray images. In someembodiments, the angiographic images are two-dimensional (2-D). In someembodiments, 2-D angiographic images taken from different viewing anglesare combined to produce a model including three-dimensional (3-D) data,for example, from 2, 3, 4 or more viewing angles.

In some embodiments, the data is from computerized tomography (CT)scans. It should be noted that with present-day technology, angiographicimages provide a finer resolution than CT scans. Models of the vascularsystem constructed based on angiographic images, whether one-dimensional(1-D) tree models or full 3-D models, are potentially more accurate thanmodels based on CT scans, and potentially provide a more accuratevascular assessment.

Speed of Results

An object in some embodiments of the present invention related toreal-time use is rapid calculation of a vascular model, and ofanatomical and/or functional parameters thereof, in order to providefeedback for real-time diagnostic decision making.

In some embodiments of the invention, the feedback relevant to making adecision for an intervention (for example, in a particular region, or atall) is dividable into three broad categories: “recommend to intervene”,“recommend not to intervene”, and “no recommendation”. Optionally,feedback is presented in such a format. Optionally, categorizationitself is performed by a physician, based on a provided index, which isa number, graph, category description, and/or another output, having anumber of output states which is a plurality of output states, acontinuous range of output states or any number of states in between.Furthermore, in some embodiments of the invention, diagnostic feedbackis related and/or easily relatable to clinical outcome, for example byproducing an index which is easily related to (and potentiallyinterchangeable with) indexes already established in the field, such asFFR_(pressure), a scoring method such as the SYNTAX score, or anothermethod of vascular assessment.

In some embodiments of the invention, vascular tree construction isoptimized for the production of vascular segment pathways, for example,vascular segment centerlines. From this stage (or from the results ofanother process producing a vascular tree from which the positions ofvascular extent are easily determined), calculations for determinationof one or more diagnostically significant indexes is potentially veryrapid, so long as appropriate index target is sought.

Related to the selection of an appropriate index target, another objectin some embodiments of the present invention related to real-time use isthe use of a flow parameter which can be calculated extremely rapidly,given a vascular tree, while still producing a diagnostic index which isaccurate enough to be usable as a clinical decision making tool. One aidto obtaining such an index, in some embodiments, is the availability ofa deep vascular tree (3, 4, or more branches), such that resistance toflow throughout a large extent of the vascular network can be calculatedwith respect to the impact on flow through a particular segment in botha stenosed (narrowed) and an astenotic (widened) state. In someembodiments, any well-defined vascular tree either constructed asdescribed herein for X-ray angiographic images, but also as potentiallyavailable from another imaging method such as rotational angiographyand/or CT angiography, potentially serves as an input for image-basedFFR computation. “Well-defined” comprises, for example, having a branchdepth of 3, 4 or more vascular branches. Additionally or alternatively,“Well-defined” comprises, for example, imaging resolution sufficient tomodel vascular width with an accuracy of within 5%, 10%, 15% or anotherlarger, smaller, or intermediate value of true vascular width.

In some embodiments of the invention, a focus on the production of atreatment recommendation guides the choice of image analysis methods,such that rapid provision of diagnostic feedback is more readilyobtained. In particular, in some embodiments, a goal is to provide ananalysis of whether or not a particular revascularization interventionwill restore clinically meaningful blood flow. It is a potentialadvantage in creating a vascular model to focus on the modeling ofmeasurable parameters which are targeted for change in a clinicalintervention, as it is due to these changes that the effects of aproposed treatment (if any) will be felt. Furthermore, such a focusoptionally allows unchanging and/or equivalent parameters to besimplified and/or disregarded, at least to the extent that they do notaffect the desirability of a treatment outcome. Thus, for example,potentially no modeling of dynamic flow is necessary to arrive at adiagnostic index of vascular function.

In some embodiments of the invention, a sufficient analysis to produce auseful recommendation for PCI and/or CABG (coronary artery bypassgrafting) comprises analysis of one or more features which are readilydetermined as a local function of a 1-D parameter such as vascularsegment position. For example: vascular resistance, while subject tomany variables potentially treatable by a full consideration of asystem's fluid dynamics, has a strong dependence on the variable ofvascular diameter. Vascular diameter in turn is a target of treatmentoptions such as stent implantation. Moreover, vascular diameter itself(and/or related metrics such as vascular radius, cross sectional areaand/or cross-sectional profile), is rapidly calculable from image dataalong a pathway comprising a description of vascular segment position.

Moreover, a potential advantage of a vascular model optimized forcenterline determination is that, for example, calculations relating toless clinically significant details, for example, vascular wall shape,are avoidable and/or postponable. In some embodiments, vascularcenterlines provide the central framework of the final model. It is apotential advantage to use the same vascular centerlines (and/orapproximations thereto) as features which provide landmarks during aphase of processing which constructs a 3-D coordinate system to which2-D images from which a 3-D vascular tree will be modeled areregistered. Potentially, this avoids a requirement for determination ofa second feature set. Potentially, using the same feature set for bothregistration and the model basis avoids some calculations to overcomeinconsistencies due to imaging artifacts, since the skew betweenregistration features and model features is thereby reduced.

In some embodiments of the invention, a full processing period from thereceipt of images to the availability of a diagnostically useful metricsuch as FFR comprises about 2-5 minutes, with the application ofrelatively modest computational resources (for example, a PC comprisingan off-the-shelf multicore CPU and 4 mid-range GPU cards—equivalent toabout 8-12 teraflops of raw computational power). On the 5 minute timescale and with this type of equipment, in some embodiments, centerlinesegmentation of about 200 input images comprises about a half minute ofprocessing time, conversion to a 3-D model about 4 minutes, andremaining tasks, such as FFR calculation, about 10-30 seconds, dependingon the extent of the tree which is calculated. It should be noted thatfurther reductions in processing time are expected so long as generalprocessing power cost per teraflop continues to decrease. Furthermore,multiprocessor and/or multicore division of processing tasks can benaturally achieved by divisions along vascular boundaries, for exampleby dividing work among processing resources based on spatial positions.In some embodiments of the invention, the computation to reconstruct avascular tree and calculate a flow index comprises less than about10,000 trillion operations. In some embodiments, the computationcomprises less than about 5,000 trillion, 2,000 trillion, 1,000trillion, 500 trillion, or an intermediate, greater, or lesser number ofoperations.

Another object of some embodiments of the present invention is theintegration of automatic vascular parameter determination from imagesinto the clinical work flow. In some embodiments, the integration isinteractive, in that it comprises interaction between results and/orcontrol of automatic imaging processing and other aspects of acatheterization procedure, while the procedure is underway. For example,in some embodiments of the invention, a medical professional candetermine from cursory manual inspection that one of two branches of avascular tree is a likely first candidate for a vascular interventionsuch as PCI. In some embodiments of the invention, the first candidatebranch is selectable such that processing to determine, for example, anFFR index for that branch completes earlier than calculations for asecond branch. Potentially, this allows decision making to occur earlierand/or with a shorter interruption in the procedures being performed ona patient.

In some embodiments of the invention, tree processing is sufficientlyrapid that two, three, or more imaging procedures can be performed andanalyzed within the course of a single session with the patient. Asingle session comprises, for example, a period during which a portionof a catheter and/or guidewire remains in a portion of a vascular tree,for example, for intervention to open a stenosis therein; the time is,for example, 30 minutes to an hour, or a lesser, greater, orintermediate period of time. FFR_(pressure), for example, is typicallydetermined in conjunction with an injection of adenosine to maximallywiden a patient's vasculature. The safe frequency of adenosine injectionis limited, however, so a method of determining an FFR-equivalent indexwithout such an injection provides a potential advantage. A secondimaging session is potentially valuable, for example, to verify theresults of a stent implantation, as is commonly performed at the levelof positioning verification for current stent implantations.Potentially, vascular autoregulation after stent implantation results inchanges to vascular width, such that a second imaging session can helpdetermine if a further stent implantation has become and/or remainsadvisable.

In some embodiments, results of intensive phases of calculation canserve as a basis for recalculation based on further acquired images,and/or recalculation of indices. For example, a previously calculatedvascular tree can serve as the basis of registration of one or moreimages of a post-implantation vasculature, without requiring a fullimage set to be re-acquired.

In some embodiments of the invention, a user interface to the computer,for example, a graphical user interface, is provided such that one ormore interactive user commands are supported. Optionally, for example,one or more user commands is available to focus image processing targetsto one or more selected branches of the subject's vasculature.Optionally, one or more commands to change an aspect of a vascular model(for example, to model an astenotic state of a stenotic vessel) isavailable. Optionally, one or more commands to select among and/orcompare vascular models from a plurality of image sets (for example,image sets taken at entirely different times during procedure, and/orimage sets comprising views of the heart at different heartbeat cyclephases) is available.

Features of Some Exemplary Vascular Models

In some embodiments of the invention, the vascular system modelcomprises a tree model; optionally a 3-D tree model. However, thespatial dimensionality of the model is optionally adjusted at differentanatomical levels and/or stages of processing to suit the requirementsof the application. For example, 2-D images are optionally combined toextract 3-D vascular tree information which allows identification andconstruction of 1-D vascular segment models. 1-D segment models in turnare logically linked, in some embodiments, according to theirconnectivity, with or without preserving details of their other spatialrelationships. In some embodiments, spatial information is collapsed orencoded; for example, by approximating a cross-sectional region by theparameters of a circle (diameter), ellipse (major/minor axis), or otherrepresentation. In some embodiments, regions along the vascular treecomprise non-spatial information, for example, flow resistance,calculated flow volume, elasticity, and/or another dynamic or staticproperty associated with an sampled and/or extended vascular segmentregion, and/or a node of the vascular tree.

In some embodiments, the tree model comprises a tree data structurehaving nodes linked by curvilinear segments. The nodes are associatedwith vascular furcations (e.g., bifurcation or trifurcations ormulti-furcations), and the curvilinear segments are associated withvessel segments. A curvilinear segment of the tree is also referred tobelow as a branch, and the entire tree portion distal to a branch isreferred to as a crown. Thus, a tree model, in some embodiments of theinvention, comprises a description of the vascular system which assignsnodes of the tree to vascular furcations and branches of the tree tovessel segments of the vascular system.

In some embodiments, sample points along branches are associated withvascular diameter information. In such embodiments, the tree can beconsidered as being represented as a series of disks or poker chips(e.g., circular or elliptical disks) that are linked together to form a3-D structure containing information relating to the local size, shape,branching, and other structural features at any point in the vasculartree.

In some embodiments, trifurcations and/or multi-furcations aremethodically converted to a combination of bifurcations. Optionally, forexample, a trifurcation is converted to two bifurcations. The term“furcation” in all its grammatical forms is used throughout the presentspecification and claims to mean bifurcation, trifurcation, ormulti-furcation.

In some embodiments, the tree model includes property data associatedwith sample points along each branch in the model, and/or aggregated forthe whole branch and/or for extended portions thereof. Property dataincludes, for example: location, orientation, cross-section, radiusand/or diameter of vessels. In some embodiments, the tree modelcomprises flow characteristics at one or more of the points.

In some embodiments, the tree model comprises geometric data measuredalong vessel centerlines of a vascular system.

In some embodiments, the vascular system model comprises a 3-D model,for example a 3-D model obtainable from a CT scan, and/or constructedfrom a set of 2-D angiographic images taken from different angles.

In some embodiments, the vascular system model comprises 1-D modeling ofvessel segments along center lines of a set of vessels of a vascularsystem.

In some embodiments, the tree model of the vascular system, comprisesdata about segments represented in 1-D which describes segment splittinginto two or more segments along a vessel.

In some embodiments the model includes a collection of data alongsegments of the vessels, including three dimensional data associatedwith a 1-D collection of points; for example: data about a crosssectional area at each point, data about a 3-D direction of a segment,and/or data about an angle of bifurcation.

In some embodiments, the model of the vascular system is used tocalculate a physical model of fluid flow, including physicalcharacteristics such as pressure, flow rate, flow resistance, shearstress, and/or flow velocity.

It is noted that performing calculations for a 1-D collection of points,such as calculations of resistance to fluid flow, is potentially muchmore efficient than performing such calculations using a full 3-D modelwhich includes all voxels of a vascular system.

Calculation of a Vascular Model

Reference is now made to FIG. 13 , which is a flow chart describing anexemplary overview of stages in vascular model construction, accordingto some exemplary embodiments of the invention.

FIG. 13 serves as an overview to an exemplary vascular treereconstruction method, which is introduced first in overview, thendescribed in more detail hereinbelow.

At block 10, in some embodiments, images are acquired, for example,about 200 images, divided among, for example, 4 imaging devices. In someembodiments, acquired images are obtained by X-ray angiography. Apotential advantage of using X-ray angiograms includes the commonavailability of devices for stereoscopic X-ray angiography in cath labswhere diagnosis and interventional procedures are performed, accordingto the current state of the art. The X-ray angiographic images alsohave, potentially, a relatively high resolution compared to alternativeimaging methods such as CT.

At block 20, in some embodiments, vascular centerlines are extracted.Vascular centerlines have several properties which make them a usefulreference for other phases of vascular tree reconstruction. Propertiestaken advantage of in some embodiments of the current inventionoptionally include the following:

-   -   Centerlines are features determinable from 2-D images, allowing        their use to relate individual images to one another in 3-D.    -   Vascular centerlines are, by definition, distributed throughout        an imaging region of interest when the target is to reconstruct        a 3-D vascular model. Thus, they serve as attractive candidates        for reference points within the reconstructed imaging region.    -   Vascular centerlines are automatically determinable, without        prior human selection, based on image properties which are        readily segmented, for example, as described hereinbelow.    -   Vascular centerlines are extended features which preserve        sufficient similarity among images, even images taken from        different views, that their homologies are readily identifiable,        for example in the 2-D images themselves, and/or by        back-projection along rays into a 3-D space, where ray        intersections (and/or intersections among dilated volumes based        on back-projected rays) identify homologous targets found in        different image projections.    -   Spatial ordering of samples along centerlines is conserved among        images even though the centerlines themselves are distorted due        to viewing angle and/or motion artifacts. This, for example,        further facilitates comparisons useful for 3-D reconstruction.    -   Centerlines comprise a convenient frame of reference for        organizing and/or analyzing features related to position along a        blood vessel. For example, using distance along the centerline        as a reference, morphological features such as diameter, and/or        functional features such as flow resistance, can be expressed as        functions in a simplified, 1-D space.    -   Intersections of centerlines provide a convenient way to        describe vascular branch points, and/or divide a vascular tree        into distinct segments which are optionally treated separately        from one another, and/or further simplified, for example, for        purposes of the functional analysis of flow properties.

Additionally or alternatively, in some embodiments, another type ofimage feature is identified. Optionally, an image feature is, forexample, a furcation of a vessel, or a location of minimal radius (alocally reduced radius compared to surrounding vascular regions) in astenosed vessel. Optionally, an image feature is any configuration ofimage pixels having a pattern of intensities generally lacking inself-identity (below a predetermined threshold of self-identity, forexample, always above a threshold of intensity difference, or alwayswithin a criterion for statistical insignificance of self-identity) overtranslation in any direction, for example, a corner-like bend orfurcation.

At block 30, in some embodiments, correspondences between extractedvascular centerlines in individual 2-D images are found. Thesecorrespondences more generally show the relationship between the 2-Dimages. Additionally or alternatively, another feature commonlyidentifiable in a plurality of 2-D images is a basis for the finding ofcorrespondences. It should be noted that such correspondences, ingeneral, are not uniquely revealed by transformations determined apriori from calibration information relating to the imaging systemand/or patient. A potential advantage of using centerlines for findingcorrespondences is that the very features of greatest interest in thevascular images (the blood vessels themselves) are the basis of thedetermination.

In some embodiments of the present invention, a surface corresponding toa shape of the heart of the subject is defined, for example by using thepattern of cardiac blood vessels to determine the projection of theheart surface into different 2-D image planes, and calculating a shellvolume therefrom. Optionally, this surface is used as a constraint forthe detection of the corresponding image features. In some embodiments,other sources of image data constraints and/or additional informationare used in the course of reconstructing a vascular tree. For example,one or more knowledge-based (atlas-based) constraints can be applied,for example, by restricting recognized vascular positions to those whichfall within a range of expected vascular positions and/or branchconfigurations. Also for example, temporal information is available insome embodiments of the invention based on the filling times ofpositions along the vascular tree. Filling time, in some embodiments, isused for determination and/or constraining of, for example, relativevascular position (position along the vascular tree extent). Fillingtime is also used, in some embodiments, to help establish homologiesamong vascular features in different 2-D images (same filling timeshould be seen in all image vantage points of homologous locations).Additionally or alternatively, filling time is used in some embodimentsof the invention to constrain vascular topology.

At block 40, in some embodiments, vascular centerlines are mapped to a3-D coordinate system. In some embodiments, mapping comprisesidentifying pairs of homologous centerline positions in different 2-Dimages which best fulfill a set of optimization criteria, for example,consistency with the constraints of epipolar geometry, and/orconsistency with vascular points with 3-D positions previouslydetermined.

At block 50, in some embodiments, blood vessel diameter is estimated. Insome embodiments, vascular diameter is calculated across sample pointsof a selected 2-D projection, and extrapolated to the wholecircumference of the blood vessel. In some embodiments, diameters acrossa plurality of projection angles are determined. In some embodiments,the projected view is selected from a single acquired image, optionallyan image where the vessel is seen at its longest, and/or visible free ofcrossings. Optionally, the projected view is synthesized from two ormore 2-D images.

Applications of a Vascular Tree

The computational procedure of the present embodiments potentiallyrequires reduced computation, relative to conventional techniques whichemploy computational fluid dynamics simulation and analysis. It isrecognized that computational fluid dynamics require substantialcomputation power and/or time. For example, several days of CPU time arerequired when fluid dynamics simulation is executed on a standard PC.While this time can be somewhat reduced using a super-computer applyingparallel processing, such a computation platform is generallyunavailable for such a dedicated use in medical facilities. Thecomputational procedure of the present embodiments is not based on fluiddynamic simulations and can therefore be implemented on a computingplatform based on common, off-the-shelf components and configured, forexample, as a standard PC, without the need for a super-computer.

The present inventors found that a tree model according to someembodiments of the present invention can be provided within less than 60minutes or less than 50 minutes or less than 40 minutes or less than 30minutes or less than 20 minutes, or less than 5 minutes, or less than 2minutes from the time at which the 2-D images are received by thecomputer. The time is potentially dependent on available computationalresources, but the inventors have found that common off-the-shelfcomputational hardware (available, for example, with an aggregatecomputational power in the range of about 8-12 teraflops) is sufficientto reach a run time of 2-5 minutes.

This allows some present embodiments of the invention to efficientlycombine between the computation and treatment, wherein the tree model isoptionally produced while the subject is immobilized on a treatmentsurface (e.g., a bed) for the purpose of catheterization. In someembodiments of the present invention, the tree model is produced whilethe subject has a catheter in his or her vasculature. In someembodiments of the present invention the vasculature has at least onecatheter other than an angiographic catheter, (for example, a cardiaccatheter or an intracranial catheter), wherein the images are processedand the tree is produced while the catheter is in the vasculature.

Use of a calculated vascular tree is envisioned in the context offurther processing and/or decision making in a clinical setting. Apotential advantage of a method and/or system for rapid determination ofa vascular tree is its usefulness in “real time” applications whichallow automatically assisted diagnostic and/or treatment decisions to bemade while an imaged patient remains immediately available—perhaps stillon the catheterization table—for a further procedure.

Examples of such real time applications include vascular flowdetermination, and/or vascular state scoring.

FFR

In some embodiments of the present invention the model calculated fromthe original imaging data is treated as a “stenotic model”, so-calledbecause it potentially reflects locations of stenosis in the patientsvascular (cardiovascular) system. In some embodiments, this stenoticmodel is used for calculating an index indicative of vascular function.The index can also be indicative of the need for revascularization. Arepresentative example of an index suitable for the present embodimentsincludes, without limitation, FFR.

In some embodiments, the index is calculated based on a volume or othervascular parameter of a crown in the stenotic model and on acontribution of a stenosed vessel to the resistance to blood flow in thecrown. In some embodiments, the FFR index is calculated as a ratio offlow resistance of a stenosed vessel in a vascular model which includesthe stenosed vessel to flow resistance of an inflated version of thesame vessel in a similar vascular model where the stenosed vessel wasmathematically inflated.

In some embodiments, the index is calculated as a ratio of flowresistance of a stenosed vessel in a vascular model to flow resistanceof a neighboring similar healthy vessel in the vascular model. In someembodiments, the ratio is multiplied by a constant which accounts fordifferent geometries of the stenosed vessel and the neighboring vessel,as described below in the section titled “Producing a model of physicalcharacteristics of a vascular system”.

In some embodiments, a first tree model of a vascular system isproduced, based on actual patient measurements, optionally containing astenosis in one or more locations of the patient's vessels, and a secondtree model of the patient's vascular system is produced, optionallychanged so that at least one of the stenosis locations is modeled asafter revascularization, and an index indicative of the need forrevascularization is produced, based on comparing physicalcharacteristics of the first model and the second model.

In some embodiments, actual pressure and/or flow measurements are usedto calculate the physical characteristics in the model(s) and/or theabove-mentioned index.

In some embodiments, no actual pressure and/or flow measurements areused to calculate the physical characteristics in the model(s) and/orthe above-mentioned index.

It is noted that resolution of angiographic images is typically higherthan resolution typically obtained by 3-D techniques such a CT. A modelconstructed from the higher resolution angiographic images, according tosome embodiments, can be inherently higher resolution, and providegreater geometric accuracy, and/or use of geometric properties ofsmaller vessels than CT images, and/or calculations using branchingvessels distal to a stenosis for more generations, or bifurcations,downstream from the stenosis than CT images.

Vascular State Scoring

In some embodiments of the invention, automated determination ofparameters based on vascular images is used to calculate a vasculardisease score. In some embodiments, the imaged blood vessels are cardiacblood vessels.

In some embodiments of the invention, a cardiac disease score iscalculated according to the SYNTAX Score calculation method. In someembodiments, a cardiac disease score is calculated by a SYNTAX Scorealternative, derivative and/or successor vascular state scoring tool(VSST). Alternative VSST approaches potentially include, for example, a“Functional SYNTAX Score” (integrating physiological measurements—forexample, vascular flow capacity, vascular elasticity, vascularautoregulatory capacity, and/or another measure of vascularfunction—with a SYNTAX Score-like tool), or a “Clinical SYNTAX Score”(integrating clinical variables—for example, patient history, and/orsystemic and/or organ-specific test results—with a SYNTAX Score-liketool). Examples also include the AHA classification of the coronary treesegments modified for the ARTS study, the Leaman score, the ACC/AHAlesions classification system, the total occlusion classificationsystem, and/or the Duke and ICPS classification systems for bifurcationlesions.

In some embodiments, two-dimensional images from an angiographicprocedure are converted into a three-dimensional image, and lesionswithin the vessel are identified and entered as VSST parameters toarrive at a quick, objective SYNTAX score during the procedure. In someembodiments, VSST parameters are determined directly fromtwo-dimensional images. Thus, for example, a 2-D image having adetermined spatial relationship to a 3-D vascular model (and optionallythereby to vascular segments identified therein) is analyzed forvascular geometry characteristics which are then linked to positions inthe vascular model (and optionally identified vascular segmentstherein).

In some embodiments of the present invention, automatically determinedvalues are provided as parameters to a VSST such as SYNTAX Score inreal-time during a catheterization procedure, or following imaging.

Potentially, a reduced time of VSST calculation provides an advantage byallowing a patient to be kept catheterized for a possible PCI(Percutaneous Coronary Intervention) treatment while waiting for ashorter period, and/or by reducing the need for recatheterization of apatient who has been temporarily released from a procedure room pendinga treatment decision. Potentially, a reduced time and/or effort ofscoring leads to increased use of a VSST such as SYNTAX Score as a toolfor clinical decision-making.

Producing a Geometric Model of a Vascular System

Image Acquisition

Reference is now made to FIG. 14 , which is a flow chart describing anexemplary overview of details of stages in vascular model construction,according to some exemplary embodiments of the invention. Detail is alsodescribed in additional figures referenced in the course of steppingthrough the blocks of FIG. 14 hereinbelow.

At block 10, a plurality of 2-D data images are acquired. In someembodiments of the invention, data images are simultaneously acquiredfrom a plurality of vantage points, for example, 2, 3, 4 or more imagingvantage points (cameras). In some embodiments, images are acquired at aframe rate of, for example, 15 Hz, 30 Hz, or another lesser, greater, orintermediate frame rate. In some embodiments, the number of framesacquired per imaging vantage point is about 50 frames (200 frames totalfor 4 imaging vantage points). In some embodiments, the number of framesper imaging vantage point is, for example, 10, 20, 40, 50, 60, 100, oranother larger, smaller, or intermediate number. In some embodiments ofthe invention, the number of heartbeat cycles comprised in an imagingperiod is about 3-4 heartbeat cycles. In some embodiments, the number ofcardiac cycles is, for example, 3-4, 3-5, 4-6, 5-10, or another range ofheartbeat cycles having the same, lesser, greater, or intermediate rangeboundaries.

Reference is now made to FIG. 1 , which depicts an original image 110and a Frangi-filter processed image 120, processed according to someexemplary embodiments of the invention.

The original image 110 depicts a typical angiogram 2-D image.

It should be noted that when using two or more 2-D projections of asubject's vessels, for example heart vessels, it is a potentialadvantage for two or more 2-D projections be taken at the same time, orat least at a same phase during a heart beat cycle, so that the 2-Dprojections be of a same vessel shape.

Deviations between the 2-D projections might arise from cardiac, and/orrespiratory and/or patient motions between the 2-D projection frames.

In some embodiments, for reduction deviations that might arise from lackof cardiac phase synchronization, an ECG output is used to choose a samecardiac phase in the 2-D projections frames.

In some embodiments, for reduction of deviations that might arise fromlack of cardiac phase synchronization, an ECG output, or anotherheart/pulse synchronization means, such as an optical pulse monitor, isused to choose a same cardiac phase in the 2-D projections frames.Optionally, a heart synchronization output is recorded with a timescale, and a corresponding time scale is used to record when images ofthe vascular system are captured. In some embodiments of the invention,time of acquisition relative to a cycle of physiological movements isused to determine candidates for mutual registration. For example, imageregistration is optionally carried out using image data sets whichcomprise images taken at nearby phases of the heartbeat cycle. In someembodiments, registration is carried out multiple times across differentsets of phase-adjacent data sets, so that registered landmarks areiteratively transformed in position to a common 3-D coordinate system.

In some embodiments, 2-D projection frames are selected to be at an endof the diastole phase of the cardiac cycle. In some embodiments, thetemporal and/or phase order in which 2-D projection frames are acquiredis used to perform registrations among images taken during adjacentmovement cycle phases. In some embodiments, images registered from afirst phase to a second phase are then re-registered into a third and/orfurther phase, such that images take at widely separated heartbeat cyclephases can be registered to one another.

In some embodiments, the heart is imaged under influence of intravenousadenosine, which potentially exaggerates a difference between normal andabnormal segments. Optionally imaging with and without adenosinepotentially allows determination of the (vascular expanding) effects ofadenosine itself, which in turn potentially provides information aboutvascular compliance and/or autoregulatory state.

Centerline Extraction

Reference is now made to FIG. 15 , which depicts a schematic of anexemplary arrangement 1500 of imaging coordinates for an imaging system,according to some exemplary embodiments of the invention.

Several different spatial relationships of an imaging arrangement areused in determining the 3-D relationship of image data in a set of 2-Dimages. In some embodiments, the image coordinate systems 1510, 1520 andassociated imaging planes 1525, 1530 describe how images taken of thesame subject at different positions relate to one another, informationwhich is used to reconstruct 3-D information about the subject. In someembodiments of the invention, these coordinates reflect the axes of theC-arm rotations of an angiogram imaging device. In some embodiments, thecoordinate plane 1515 of the subject (for example, lying on bed 1505) isalso used as part of a 3-D reconstruction.

Although this system configuration information is typically documentedin a DICOM (image) file and/or elsewhere, it is not guaranteed toreflect the real position and orientation of the system components withsufficient accuracy and/or precision for useful reconstruction of acoronary artery tree. In particular, the bed axes are potentiallymisaligned with the room coordinate system, the axes of the C-armrotations potentially do not intersect at the iso-center and/or arenon-orthogonal, and/or the detector axes are potentially not aligned inplane.

At block 20—returning to FIG. 14 —centerlines of a vascular tree areextracted from the acquired 2-D images. In some embodiments of theinvention, image filtering by anisotropic diffusion 21 comprises aportion of the processing operations which precede centerlineextraction. Anisotropic diffusion of 2d gray-scale images reduces imagenoise while preserving region edges—smoothing along the image edges, andremoving gaps due to noise. In some embodiments, the basis of the methodused is similar to that introduced by Weickert in “A Scheme forCoherence-Enhancing Diffusion Filtering with Optimized RotationInvariance” and/or “Anisotropic Diffusion in Image Processing” (Thesis1996).

Reference is now made to FIG. 16 , which is a simplified flowchart ofprocessing operations comprised in anisotropic diffusion, according tosome exemplary embodiments of the invention.

Operations are described for a single image, for some embodiments of theinvention. At block 21A, the Hessian transform is calculated from everypixel of the Gaussian smoothed input image (the Hessian is related tothe 2^(nd) derivative of the image data, and is a form of edgedetection). At block 21B, the Hessian-transformed image is smoothed, forexample, by a Gaussian filter. At block 21C, the eigenvectors andeigenvalues of the smoothed Hessian-transformed image are calculated.The resulting eigenvalues are in general larger where the original imagecomprises edges, while eigenvectors corresponding to large eigenvaluesdescribe the direction in which the edge runs. Additionally oralternatively, another edge detection method, such as are known in theart, is used.

At block 21D, in some embodiments of the invention, a diffusion image iscalculated. A finite difference scheme is used to perform the diffusion,using, in some embodiments, the eigenvectors as diffusion tensordirections.

At block 21E, in some embodiments, a determination is made if adiffusion time limit (for example, a certain number of iterations whichlead to a desired level of image filtering) has been reached. If no, theflowchart returns to block 21A and continues. If yes, the flowchartends—and flow continues within a higher-level flowchart, for example,that of FIG. 14 .

At block 22, in some embodiments of the invention, a Frangi filter isapplied, based on the Hessian eigenvectors, which comprises computationof the likeliness of an image region to be within a vessel. Frangifiltering is described, for example, by Frangi, et al. “Multiscalevessel enhancement filtering”, Medical Image Computing andComputer-Assisted Intervention—MICCA'98. By way of a non-limitingexample, the Frangi-filter processed image 120 (FIG. 1 ) depicts theoriginal image 110 after image enhancement using a Frangi filter. Insome embodiments, another filter is used, for example a thresholdfilter, or a hysteresis threshold filter, whereby pixels of an image areidentified as belonging within an image region of a vessel.

At block 23, in some embodiments of the invention, the image isprocessed to generate a black-white figure representing vascularlocations in the angiographic projection image. In some embodiments, ahysteresis threshold filter is performed on the Frangi filter outputwith high and low thresholds. First the algorithm detects the pixelswhich are (for example, with reference to image 120) brighter than thehigher threshold; these are labeled as vascular pixels. In the secondstep, the algorithm also labels as vascular those pixels with brightnesshigher than the low threshold, and also connected across the image topixels already labeled as vascular pixels.

A potential disadvantage of the Frangi-filter is the presence ofbulb-like shapes at vascular junctions which interfere with accuratedetection. In some embodiments, a region-grow algorithm is used toextract these regions, as an improvement on hysteresis thresholdingalone. The thresholded black-white image and the gray-scale imageobtained by anisotropic diffusion comprise inputs to this algorithm.

Square dilation is performed on the black-white image, and the result issubtracted from the original black-white image. The subtraction imagecomprises a one pixel-wide frame along which the growth of the region ofvascular-labeled pixels is then examined. The values (brightnesses) ofthe pixels in this frame are compared locally to those of existingvascular pixels, and to the surrounding pixels. A high relative resultleads to expansion. Optionally, the process repeats until no morevascular pixels are found.

At block 24, in some embodiments, a thinning convolution is applied tothin the black-white image segments down to lines which represent thevascular centerlines. In some embodiments, blocks 21-24 are performedper image (for example, sequentially, interleaved, and/or in parallel).At block 25, assuming sequential processing, if there are more images toprocess, the next image is selected at block 26, and processingcontinues again with block 21.

Otherwise, centerline extraction, in some embodiments of the invention,is complete. Reference is now made to FIG. 2 , which depicts a 2-D tree218 comprising light-colored vascular centerlines overlaid on top of theoriginal image 110 of FIG. 1 , according to an example embodiment of theinvention.

Motion Compensation

In some embodiments of the invention, processing to find centerlinecorrespondences continues (FIG. 14 ) at block 30. A goal of findingcenterline correspondences is to find correspondences between different2-D images (points that image the same region of space, thoughpotentially from different angles), such that a 3-D reconstruction ofthe target vasculature can be made.

At block 31, operations for motion compensation and/or imaging positionartifact compensation are performed.

With ideal calibration information (each image plane perfectlyidentified relative to a common coordinate axis, for example), and noartifacts due to motion or other positioning errors, back-projecting alarge enough number of 2-D images to 3-D space potentially yieldsintersecting rays (for example, rays S₁-P₁ and S₂-P₂ of FIG. 20B)uniquely defining the extents of the vascular centerline in 3-D. Inpractice, deviations among images originate, for example, frombreathing, voluntary movements of the patient, and inaccurate and/orimprecise phase-locking of imaging exposures to the cardiac cycle.Overcoming this issue in a computationally inexpensive way is a goal, insome embodiments, of operations for motion compensation. Calibrationerrors potentially introduce other forms of image position artifacts.

In some embodiments of the invention, the procedure compensates forbreath and/or other patient movement. Optionally, this comprisesiteratively repeating the detection of the corresponding image features,each time for a different subset of angiographic images, and updatingthe image correction parameters responsively to the repeated detection.

Reference is now made to FIG. 17A, which is a simplified flowchart ofprocessing operations comprised in motion compensation, according tosome exemplary embodiments of the invention.

At block 31A, in some embodiments of the invention, a subset of images(comprising a plurality) with identified 2-D centerlines is selected forprocessing. Centerlines are optionally dilated at block 31B, and acenterline projection back into 3-D performed at block 31C, based on thecurrent best-known projection parameters for each image (initially theseare, for example, the parameters expected based on the knowncalibrations of the imaging device). The resulting projected volume isskeletonized, in some embodiments, to form a “consensus centerline” atblock 31D. At block 31E, the consensus centerline is projected back intothe coordinate systems of 2-D images comprising those which were notused in forming the consensus centerline. At block 31F, an optimizationprocedure adjusts projection parameters for the 3-D centerline into each2-D image to fit more closely centerlines found within the image itself.This adjustment is used to adjust the projection parameters associatedwith each image. At 31G, in some embodiments, a determination is madewhether or not to repeat the procedure for a different image subset, forimprovement of the overall quality of the projection fits. Thedetermination to repeat is based, for example, on a predetermined numberof iterations, a metric measuring quality of fit (such as a meandistance between closest points in centerline projections), or anothercriterion. If yes, the flowchart returns to block 31A and continues. Ifno, the flowchart ends—and flow continues within a higher-level orderingof operations, for example, that of FIG. 14 .

It was found by the present inventors that such an iterative processsignificantly can reduce one or more of the effects of breath, patientmovements, and heart phase difference.

Reference is now made to FIG. 17B, which is a simplified flowchart ofprocessing operations comprised in an alternative or additional methodof motion compensation, according to some exemplary embodiments of theinvention.

In some embodiments of the invention, at block 31H, features areidentified in a reference image R based on any feature detection methodknown in the art. Such an image feature is, for example, a furcation ofa vessel, and origin of the coronary vessel tree, a location of minimalradius in a stenosed vessel, and/or any configuration of image pixelshaving a pattern of intensities generally lacking in self-identity overtranslation in any direction—for example, a corner-like bend orfurcation. Similar features (putatively homologous to those of thereference image) are identified in remaining images F.

In some embodiments of the invention, at block 31I, images in F are thenregistered to image R. For example, the best-known projection parametersof image F are used to transform into the best-known projection plane ofimage R, and then optimized to obtain an improved fit, for example usingepipolar geometry to calculate parameters of shift, rotation, and/orscaling. Optionally, registration comprises application of a geometricdistortion function. The distortion function is, for example, a first,second, or other-order function of the two image plane coordinates.Additionally or alternatively, the distortion function comprisesparameters describing the adjustment of nodal points defined within theimage coordinate planes to bring them into registration. In someembodiments, the best-known projection parameters are the same, and onlythe geometric distortion function is applied.

In some embodiments, operations at block 31I comprise image correctionparameters calculation based on identified corresponding image features.Correction parameters typically describe, for example, translationand/or rotation of the system of coordinates of a particular image.Based on the calculated parameters, the angiographic images areregistered to provide to provide mutual geometrically correspondencethereamongst. In some embodiments of the invention, several images areregistered relative to one of the images. For example, whencorresponding image features are identified in N images (e.g., N=2, 3, 4or more) one of the images can be selected as a reference, while theregistration is applied for the remaining N−1 angiographic images suchthat each of those remaining images geometrically corresponds to thesingle angiographic image that was selected as a reference. In someembodiments, another registration scenario, for example, pairwiseregistration, is performed.

At block 31J, in some embodiments, candidate feature positions ofidentified features which are expected to be comprised within ashell-like volume near the heart surface (in particular, vascularfeatures) are filtered based on whether or not they do indeed fit withinsuch a volume. Calculation of this shell-like volume is described, forexample, in relation to FIGS. 18A-18C.

At block 31K, in some embodiments, a determination is made whether ornot to repeat the procedure for a different image subset, forimprovement of the overall quality of the projection fits. Thedetermination to repeat is made, for example, as described for block31G. If yes, the flowchart returns to block 31H and continues. If no,the flowchart ends—and flow continues within a higher-level ordering ofoperations, for example, that of FIG. 14 .

Heart Surface Constraint

Reference is now made to FIGS. 18A-18B, which illustrate aspects of thecalculation of a “cardiac shell” constraint for discarding bad rayintersections from calculated correspondences among images, according tosome exemplary embodiments of the invention.

Also, reference is now made to FIG. 18C, which is a simplified flowchartof processing operations comprised in constraining pixel correspondencesto within a volume near the heart surface, according to some exemplaryembodiments of the invention.

In some imaging procedures, a “large enough” number of projections ispotentially unavailable, such that the error in the determined positionof ray intersections potentially prevents convergence to a correctoutput. At block 32, in some embodiments of the invention, operations toreduce the effect of this source (and/or other sources) of positionalerror are performed, based on a heart surface constraint.

At block 32A, according to some embodiments of the invention, an imagewhich comprises features expected to be within the projected outline ofthe heart is selected. In some embodiments, the features arerepresentations of the coronary arteries 452, which course over theheart surface. In some embodiments, previously determined vascularcenterlines 451 comprise the identified features. At block 32B, in someembodiments, the convex hull 450 defined by the vascular centerlines 451is determined. This hull grossly represents the shape of the heart(where it is covered by identified artery centerlines) as projected intothe plane of the selected 2-D image. At block 32C, in some embodiments,a determination is made as to whether or not another image should beselected to determine the heart shell projection from a different angle.The number of images for which the convex hull of the heart shape iscalculated is at least two, in order to allow 3-D localization of theheart shell; optionally more images, and optionally all available imagesare used for heart shell determination. If another image is to be added,the flowchart continues at block 32A; if no, flow continues to block32D.

At block 32D, in some embodiments, the 3-D hull position (heart shell)is determined from the various available 2-D hull projections, forexample by using the best-known projection parameters for each 2-D imageplane, and/or the intersections of the 3-D polyhedra. Such a surface canbe defined using any technique known in the art, including, withoutlimitation, polyhedra stitching, based on the descriptions providedherein. At block 32E, in some embodiments, the heart shell is dilated toa volume, the amount of dilation being determined, for example, ascorresponding to an error limit, within which “true” vascular regionsare expected to fall.

At block 32F, in some embodiments, candidate 3-D positions of vascularcenterline points which fall outside the heart shell are excluded. Theflowchart of FIG. 18C ends—and flow continues within a higher-levelordering of operations, for example, that of FIG. 14 .

Identification of Homologies

Reference is now made to FIGS. 19A-19D, which illustrate identificationof homology among vascular branches, according to some exemplaryembodiments of the invention.

Also, reference is now made to FIG. 19E, which is a simplified flowchartof processing operations comprised in identifying homologous regionsalong vascular branches, according to some exemplary embodiments of theinvention.

At block 33A, in some embodiments, a base 2-D image is selected forhomology determination. The initial base selection is optionallyarbitrary. At block 33B, in some embodiments, the vascular centerlinesin one of the remaining images is projected into the plane of the baseimage. For example, exemplary vascular centerline 503 of FIG. 19A isfrom a base image having a base coordinate system 504. Vascularcenterline 501, taken from another image having a different coordinatesystem 502 is shown transformed into coordinate system 504 (translatedin one direction for clarity in FIG. 19A) as centerline 501B. In FIG.19B, the two centerlines are shown overlaid, illustrating their generalsimilarity, and some artifactual differences where they diverge.

At block 33C, in some embodiments, the projected vascular centerline501B is dynamically dilated 501C, noting where intersections with thebase image vascular centerline first occurs, and continuing, forexample, until all homologies have been identified. Dynamic dilationcomprises, for example, gradual expansion of the centerline, for exampleby application of a morphological operator to pixel values of the image.In some embodiments, another method, for example, a nearest-neighboralgorithm, is used (additionally or alternatively) to determinecorrespondences. FIG. 19D shows examples of correspondence betweenvascular centerline points at either end of minimal distance lines 515and 510.

At block 33D, in some embodiments, a determination is made as to whetheranother image is to be selected for dilation to the current base image.If yes, the flowchart continues at block 33B. If no, at block 33E, adetermination is made as to whether another base image is to beselected. If yes, the flowchart continues at block 33A. If no, theflowchart ends—and flow continues within a higher-level ordering ofoperations, for example, that of FIG. 14 .

It should be understood that the operations in block 30 (for example, ofsub-blocks 31, 32, 33) to find centerline correspondences are operationswhich perform the function of finding correspondences between thedifferent 2-D images—and more particularly, in some embodiments, betweenvascular centerlines in the 2-D images—which allow them to bereconstructed into a 3-D model of the vasculature. It should beunderstood that this function can be performed by variations on themethods described and/or by other methods familiar to one skilled in theart, based on the teachings of the present description. For example,wherever an image A is projected, mapped, or otherwise transformed tothe coordinate space of an image B, it is possible in some embodimentsfor the transformation be reversed (transforming B instead), or for thetransformation to be of both images to a common coordinate space. Alsofor example, features and/or locations which are nearby a feature namedin describing an operation (for example, vascular boundaries in relationto vascular centerlines) are usable, in some embodiments, to performsome of the work of finding correspondences. Furthermore, operationswhich serve entirely to refine a result (including an intermediateresult) are optional in some embodiments; additionally or alternatively,other operations to refine a result (including an intermediate result)are potentially determinable by someone skilled in the art, workingbased on the descriptions herein. These examples of variations are notexhaustive, but are rather indications of the breadth of the methodscomprising embodiments of the present invention.

In considering even more generally the results of operations describedin connection with blocks 20 and 30: progress toward at least tworelated but separate goals is calculated, in some embodiments, on thebasis of shared intermediate results—the vascular centerlines. A firstgoal is finding the spatial relationships by which acquired 2-D imagesare related to a common, 3-D imaging region. While, in principle, alarge number of possible reference features are selectable from the 2-Dimages as a basis of this determination, it is a potential advantage touse the centerlines of the vascular tree as the reference. Inparticular, determination of the vascular tree within this 3-D space isitself a second goal: thus, in some embodiments, the feature by whichimages are registered to one another is also the feature which serves asthe skeleton of the vascular model itself. This is a potential advantagefor speed of calculation, by reducing the need for separatedetermination of features for image registration, and vascular featuresas such. It is a potential advantage for the accuracy, precision, and/orconsistency of the resulting vascular model, since registration amongvascular features is the foundation of the transformations of image datafrom which those same features are to be reconstructed.

3-D Mapping

At block 40, in some embodiments of the invention, 3-D mapping of 2-Dcenterlines is performed. In some embodiments, at block 41, 3-D mappingbegins with identification of optimal projection pairs. Where severaldifferent images have been acquired, there are potentially severaldifferent (although homologous) projections of each region of a vascularcenterline into 3-D space, each based on a different pair of 2-D images.

Reference is now made to FIG. 20A, which is a simplified flowchart ofprocessing operations comprised in selecting a projection pair along avascular centerline, according to some exemplary embodiments of theinvention. Entering block 41, an initial segment comprising a vascularcenterline is chosen, along with an initial homologous group ofcenterline points along it (for example, a point from an end) in thedifferent 2-D images.

At block 41A, in some embodiments of the invention, a point P₁ on avascular centerline (corresponding to some homologous group ofcenterline points P) is selected from a first base image. At block 42B,in some embodiments, other points P₂ . . . P_(N) are selected from thehomologous group P to be paired with P₁ to find a position in 3-D space.

Reference is now made to FIG. 20B, which is a schematic representationof epipolar determination of 3-D target locations from 2-D imagelocations and their geometrical relationships in space, according tosome exemplary embodiments of the invention.

A point P₁, associated with image plane 410 is matched to a point P₂ todetermine a location P_(1,2) in 3-D space, using principles of epipolargeometry. In brief: the ray passing from a source S₁ through a targetregion to point P₁ is on a plane 417 which is determined also by rayspassing from S₂ to intersect it. The continuations of these raysintersect plane 412 along an epipolar line 415.

At block 41C, in some embodiments, points are evaluated for theirrelative suitability as the optimal available projection pair to extenda vascular centerline in 3-D space.

In some embodiments of the invention, a criterion for the optimal choiceof a projection point is a distance of a projected point from itsassociated epipolar line. Ideally, each point P₂ . . . P_(N) lies on theepipolar line corresponding to the epipolar plane defined by S₂ . . .S_(N). However, due to imaging position artifacts—for example, thosedescribed in relation to calibration and/or motion—there may remain someerror, such that a point P₁, previously determined to be homologous toP₁, lies off of its associated epipolar plane 418, and therefore adistance 420 away from its associated epipolar line 419. Optionally, theprojection point closest to its associated epipolar line for a givenhomology group is scored as most suited as the projection point forextending the vascular centerline.

In some embodiments of the invention, one or more criteria for theoptimal choice of a projection point relate to the continuity ofextension that a projected point provides from projected points alreadydetermined. For example, a set of points along vascular centerline 421A,421B can be used to determine a current direction of extension 423,and/or expected distance interval to the next extending point in 3-Dspace. In some embodiments, projection points which more closely matchone or more of these, or another geometrical criterion, are scored ascorrespondingly more suitable choices.

In some embodiments, a plurality of criteria are weighted together, anda choice of an optimal projection pair made based on the weightedresult.

At block 41D, it is determined whether a different base point in thehomology group should be chosen. If yes, the next base point is chosen,and further projections and evaluations continue from block 41A. If no,the point having the optimal (most suited from among the availablechoice) score for inclusion in the 3-D vascular centerline is chosen.The flowchart of FIG. 20A ends—and flow continues within a higher-levelordering of operations, for example, that of FIG. 14 .

At block 42, in some embodiments, the current vascular segmentcenterline is extended according to point specified by the identifiedoptimal pair of projections.

Vascular centerline determination continues at 43, in some embodiments,where it is determined whether or not another sample (homology group)should be assessed for the current vascular centerline. If so,operations continue by selection of the next sample at block 44,continuing with re-entering block 41. If not, determination is made atblock 45 whether the last vascular segment centerline has beendetermined. If not, the next segment is selected at 46, and processingcontinues at the first sample of that segment at block 41. If yes, flowcontinues, in some embodiments, to vessel diameter estimation at block50.

Vessel Diameter Estimation

Reference is now made to FIG. 21 , which is a simplified flowchart ofprocessing operations comprised in generating an edge graph 51, and infinding connected routes along an edge graph 52, according to someexemplary embodiments of the invention.

Entering block 51, in some embodiments, an edge graph is to bedetermined. At block 51A, in some embodiments, a 2-D centerlineprojection is chosen which is mapped to locations relative to thelocations of intensity values of the 2-D imaging data. Optionally, theprojection chosen is one in which the blood vessel is projected at amaximum length. Optionally, the projection chosen is one in which theblood vessel does not cross another vessel. In some embodiments,projections are chosen according to sub-regions of a 2-D centerline of avascular segment, for example, to have maximum length and/ornon-crossing properties within the sub-region. In some embodiments ofthe invention, images from orthogonal projections (and/or projectionshaving another defined angular relationship) are selected.

At block 51B, in some embodiments, a starting vascular width (a radius,for example) is estimated. The starting width is determined, forexample, by generating an orthogonal profile to the center line andchoosing the peak of the weighted sum of the first and secondderivatives of the image intensity along the profile.

At block 51C, in some embodiments, an orthogonal profile is built forpoints along the centerline; for example, for points sampled atintervals approximately equivalent to the vascular starting width. Theprecise choice of interval is not critical: using the radius as theinterval is generally appropriate to provide a sufficient resolution fordiameter estimation.

At block 51D, in some embodiments, orthogonal profiles for sampledpoints are assembled in a rectangular frame, somewhat as though theconvolutions of the 3-D centerline were straightened, bringing theorthogonal profiles through the centerline into parallel alignment.

Entering block 52, in some embodiments, connected routes along vascularedges are now found. At block 52A, in some embodiments, a first side(vascular edge) is chosen for route tracing. In some embodiments, atblock 52B, a route is found along the edge at approximately the distanceof the initial radius, for example, by minimizing the energy thatcorresponds to a weighted sum of the first and second horizontalderivatives, optionally with the aid of an algorithm of the Dijkstraalgorithm family. At block 52C, if the second side has not beencalculated, the flowchart branches to select the second side at block52D, and repeat the operation of 52B.

Continuing, in some embodiments, at block 53 of FIG. 14 , the centerlineis reset to the middle of the two vascular walls just determined. Atblock 55, in some embodiments, a determination is made if this is thelast centerline to process. If not, in some embodiments, the nextsegment is selected at block 56 and processing continues at block 51. Ifyes, at block 54, in some embodiments, a determination is made if theprocedure is to be repeated. If yes, a first segment is selected for asecond iteration, and operations continue at block 51. Otherwise, theflowchart of FIG. 14 ends.

Construction of a Segment-Node Representation of a Vascular Tree

Reference is now made to FIG. 3A, which is an image 305 of a coronaryvessel tree model 310, produced according to an example embodiment ofthe invention.

Reference is now also made to FIG. 3B, which is an image of a coronaryvessel tree model 315 of FIG. 3A, with tree branch tags 320 addedaccording to an exemplary embodiment of the invention.

It is noted that the tags 320 are simply one example method for keepingtrack of branches in a tree model.

In some embodiments of the invention, after reconstruction of a vesseltree model, such as a coronary tree, from angiographic images, the treemodel is optionally divided into branches, where a branch is defined asa section of a vessel (along the frame of reference established by thevascular centerline, for example) between bifurcations. The branches arenumbered, for example, according to their generation in the tree. Branchpoints (nodes), in some embodiments of the invention, are determinablefrom points of the skeletal centerline representation which connect inmore than two directions.

In some embodiments of the invention, branch topology comprises divisionof a vascular tree model into distinct branches along the branchstructure. In some embodiments, branch topology comprises recombinationof branches, for example, due to collateral and/or shunting bloodvessels.

Reference is now also made to FIG. 3C, which is a simplifiedillustration of a tree model 330 of a coronary vessel tree, producedaccording to an example embodiment of the invention.

For some aspects of the application of a vascular tree model to coronaryartery diagnosis and/or functional modeling, it is useful to abstractaway some details of spatial position in order to simplify (and, in thepresent case, also to illustrate) calculations of vascular treeproperties.

In some embodiments, the tree model is represented by a 1-D array. Forexample, the 9-branch tree in FIG. 3C is represented by a 9-elementarray: a=[0 1 1 2 2 3 3 4 4], which lists tree nodes in a breadth-firstorder.

In some embodiments, during a reconstruction process, spatial locationand radius of segments on each branch are sampled every small distance,for example every 1 mm, or every 0.1 mm to every 5 mm.

In some embodiments, tree branches, corresponding to vessel segmentsbetween modeled bifurcations, correspond to vessel segments of a lengthof 1 mm, 5 mm, 10 mm, 20 mm, 50 mm, and even longer.

In some embodiments, sampling every small distance increases theaccuracy of a geometric model of the vessels, thereby increasingaccuracy of flow characteristics calculated based on the geometricmeasurements.

In some embodiments, the tree model is a reduced tree, limited to asingle segment of a vessel, between two consecutive bifurcations of thevascular system. In some embodiments, the reduction is to a region of abifurcation, optionally comprising a stenosis.

Measuring Flow from Time Intensity Curves in Angiographic Sequences

In some embodiments, a physical model of fluid flow in the coronaryvessel tree is calculated, including physical characteristics such aspressure and/or flow rate, and/or flow resistance, and/or shear stress,and/or flow velocity.

In an example embodiment, a techniques based on the analysis ofconcentration-distance-time curves is used. The techniques perform wellin conditions of pulsatile flow. An example concentration-distance-timecurve technique is the concentration-distance curve matching algorithm.

Using the above-mentioned technique, a concentration of contrastmaterial, such as iodine, present at a particular distance along avessel segment, is found by integrating pixel intensities inangiogram(s) across a vessel lumen perpendicular to the centerline. Anoptimal shift is found in a distance axis between consecutiveconcentration-distance curves. A blood flow velocity is then calculatedby dividing the shift by the time interval between the curves. Severalvariations of the above technique have been reported in the followingreferences, the contents of which are hereby incorporated herein byreference: The four above-mentioned articles by Seifalian et al; theabove-mentioned article titled: “Determination of instantaneous andaverage blood flow rates from digital angiograms of vessel phantomsusing distance-density curves”, by Hoffmann et al; the above-mentionedarticle titled: “Comparison of methods for instantaneous angiographicblood flow measurement”, by Shpilfoygel et al; and the article titled:“Quantitative angiographic blood flow measurement using pulsedintra-arterial injection”, by Holdsworth et al.

Measuring Flow Using Other Modalities

In some embodiments flow is calculated from ultrasound measurements.Several variations of the above mentioned ultrasound technique have beenreported in above-references, the contents of which are herebyincorporated herein by reference: the above-mentioned article titled:“Noninvasive Measurement of Coronary Artery Blood Flow Using CombinedTwo-Dimensional and Doppler Echocardiography”, by Kenji Fusejima; thearticle titled: “New Noninvasive Method for Coronary Flow ReserveAssessment: Contrast-Enhanced Transthoracic Second Harmonic EchoDoppler”, by Carlo Caiati et al; the article titled: “Validation ofnoninvasive assessment of coronary flow velocity reserve in the rightcoronary artery—A comparison of transthoracic echocardiographic resultswith intracoronary Doppler flow wire measurements”, by Harald Lethena etal; the article titled: “Coronary flow: a new asset for the echo lab?”by Paolo Vocia et al; the review paper titled: “Non-invasive assessmentof coronary flow and coronary flow reserve by transthoracic Dopplerechocardiography: a magic tool for the real world”, by Patrick Meimounet al; and the article titled: “Detection, location, and severityassessment of left anterior descending coronary artery stenoses by meansof contrast-enhanced transthoracic harmonic echo Doppler”, by CarloCaiati et al.

In some embodiments, other modalities of measuring flow in the coronaryvessel tree are used. Example modalities include MRI flow measurementand SPECT (Single-photon emission computed tomography), or gamma camera,flow measurement.

It is noted that in some embodiments a vascular flow model isconstructed without using flow measurements or pressure measurements,based on geometrical measurement taken from images of the vascularsystem.

It is noted that in some embodiments flow measurements are used toverify flow characteristics calculated based on a model constructedbased on the geometric measurements.

It is noted that in some embodiments pressure measurements are used toverify flow characteristics calculated based on a model constructedbased on the geometric measurements.

An Example Embodiment of Producing a Model in which Stenoses are Modeledas if they had been Revascularized—Stenosis Inflation

In some embodiments an estimation is made of a structure of a sickvessel as if the vessel has been revascularized to a healthy structure.Such a structure is termed an inflated structure—as if a stenosed vesselhas been revascularized back to normal diameter.

In some embodiments a technique is used as described in the followingreferences, the contents of which are hereby incorporated herein byreference: the article titled “Dedicated bifurcation analysis: basicprinciples”, by Tuinenburg et al; the article titled “QuantitativeCoronary Angiography in the Interventional Cardiology”, by Tomasello etal; and the article titled “New approaches for the assessment of vesselsizes in quantitative (cardio-)vascular X-ray analysis”, by Janssen etal.

A stenosis inflation procedure is optionally implemented for each one ofthe 2-D projections separately. In some cases a stenosis may occur in aregion nearby to a bifurcation and in some cases the stenosis may occuralong a vessel. The stenosis inflation procedure in the two cases is nowdescribed separately.

If the stenosis is not located at a bifurcation region, it is enough toassess flow in the sick vessel. Coronary vessel segments proximal anddistal to the stenosis are relatively free of disease and are referredto as reference segments. An algorithm optionally calculates a coronaryedge by interpolating the coronary vessel segments considered free fromillness located proximally and distally to the region of stenosis withthe edges of the region of the stenosis. The algorithm optionallyreconstructs a reference coronary segment that is as if free fromdisease.

In some embodiments the technique includes calculation of a mean valueof the diameters of a vessel lumen in the segment of reference locatedupstream and downstream to the lesion.

If the stenosis is located at a bifurcation region, two examplebifurcation models are defined: a T-shape bifurcation model and aY-shape bifurcation model.

The bifurcation model, T-shape or Y-shape, is optionally detected byanalyzing arterial contours of three vessel segments connected to thebifurcation. Calculation of a flow model for an inflated healthy vesseldiameter is based on calculating as if each of the three segmentsconnected to the bifurcation has a healthy diameter. Such a calculationensures that both a proximal and a distal main (interpolated) referencediameter are based on arterial diameters outside the bifurcation core.

A reference diameter function of a bifurcation core is optionally basedon a reconstruction of a smooth transition between the proximal and thedistal vessel diameters. As a result, the reference diameter of theentire main section can be displayed as one function, composed of threedifferent straight reference lines linked together.

An Example of Producing a Model of Physical Characteristics of aVascular System

Some example embodiments of methods for producing a model of physicalcharacteristics of a vascular system are now described.

An example vascular system which will be used in the rest of thedescription below is the coronary vascular system.

In some embodiments of the invention, in order to evaluate an FFR indexin a stenosed branch of a vessel tree, a one dimensional model of thevessel tree is used to estimate the flow in the stenosed branch beforeand optionally also after stent implantation.

In some embodiments of the invention, in order to evaluate an FFR indexin a stenosed branch of a vessel tree, a one dimensional model of thevessel tree is used to estimate the flow in the stenosed branch beforeand optionally also after stenosis inflation.

Based on maximal peak flow of 500 mL/min and artery diameter of 5 mm, amaximal Reynolds number of the flow is:

$\begin{matrix}{{Re_{{peak}\_{flow}}} = {\frac{4Q_{{peak}\_{flow}}}{\pi d_{\max}v} = {\frac{4 \cdot 500_{m{L/\min}}}{\pi \cdot 5_{mm} \cdot 3.5_{cP}} \approx 600}}} & {{Equation}5.1}\end{matrix}$

The above calculation assumes laminar flow. In laminar flow it isassumed, for example, that blood is a Newtonian and homogenous fluid.Another assumption, which optionally may be made, is that flow in vesselbranches is 1-D and fully developed across the cross section of thevessel.

Based on the assumptions, a pressure drop in each segment of a vesseltree is approximated according to Poiseuille formulation in straighttubes:

$\begin{matrix}{{\Delta P_{i}} = {{\frac{128\mu L_{i}}{\pi \cdot d_{i}^{4}}Q_{i}} = {\Re_{i}Q_{i}}}} & {{Equation}5.2}\end{matrix}$

Where

_(i) is a viscous resistance to flow of a segment of the vessel. Minorlosses, due to bifurcations, constrictions and curvatures of the vesselsare optionally added as additional resistances in series, according tothe Darcy-Weisbach formulation:

$\begin{matrix}{{\Delta p} = {{\frac{\rho V^{2}}{2} \cdot {\sum K_{i}}} = {\frac{8\rho Q^{2}}{\pi^{2}d^{4}} \cdot {\sum K_{i}}}}} & {{Equation}5.3}\end{matrix}$ $\begin{matrix}{{\Re(Q)} = {\frac{\Delta p}{Q} = {\left( {\frac{8\rho}{\pi^{2}d^{4}} \cdot {\sum K_{i}}} \right) \cdot Q}}} & {{Equation}5.4}\end{matrix}$

where K_(i) are corresponding loss coefficients.

Reference is now made to FIG. 4 , which is a set of nine graphicalillustrations 901-909 of vessel segment radii produced according to anexample embodiment of the invention, along the branches of the coronaryvessel tree model 330 depicted in FIG. 3C, as a function of distancealong each branch. Relative distance along the vessel segment is plottedin the X direction (horizontally), and relative radius is plotted in theY direction (vertically)

The resistance of a branch to flow is calculated as the sum of theindividual resistances of segments along the branch:

$\begin{matrix}{\Re_{branch} = {{\int_{L}{\frac{8\mu}{\pi r^{4}}{dl}}} = {\frac{8\mu}{\pi}{\int_{L}\frac{dl}{{r(l)}^{4}}}}}} & {{Equation}5.5}\end{matrix}$ or $\begin{matrix}{\Re_{branch} = {\frac{8 \times 0.035_{{\mathcal{g}}/{{cm} \cdot s}}}{\pi}{\sum\frac{{dl}_{i}}{r_{i}^{4}}}}} & {{Equation}5.6}\end{matrix}$

A resistance array corresponding to the example depicted in FIG. 3C is:

_(s)=[808 1923 1646 1569 53394 10543 55341 91454 58225], where theresistance to flow is in units of mmHg*s/mL.

The above resistance array is for a vessel with stenosis, as evidencedby a peak of 91454 [mmHg*s/mL] in the resistance array.

A resistance array for a tree model without stenosis is optionallycalculated, based on Quantitative Coronary Angiography (QCA) methods forremoving stenoses greater than 50% in area.

In some embodiments, a tree model without stenosis is optionallycalculated by replacing a stenosed vessel by an inflated vessel, thatis, geometric measurements of a stenosed vessel section are replaced bymeasurements appropriate for an inflated vessel.

In some embodiments geometric data (diameter and/or cross-sectionalarea) which is used for the inflated vessel is a maximum of thegeometric data of the unstenosed vessel at a location just proximal tothe stenosed location and at a location just distal to the stenosedlocation.

In some embodiments geometric data (diameter and/or cross-sectionalarea) which is used for the inflated vessel is a minimum of thegeometric data of the unstenosed vessel at a location just proximal tothe stenosed location and at a location just distal to the stenosedlocation.

In some embodiments geometric data (diameter and/or cross-sectionalarea) which is used for the inflated vessel is an average of thegeometric data of the unstenosed vessel at a location just proximal tothe stenosed location and at a location just distal to the stenosedlocation.

In some embodiments geometric data (diameter and/or cross-sectionalarea) which is used for the inflated vessel is calculated as a linearfunction of the geometric data of the unstenosed vessel between alocation proximal to the stenosed location and a location distal to thestenosed location, that is, the inflated value is calculated taking intoaccount distances of the stenosed location from the proximal locationand from the distal location.

A stented, also termed inflated, resistance array for the exampledepicted in FIG. 4 is:

_(n)=[808 1923 1646 1569 53394 10543 55341 80454 51225].

The peak resistance, which was 91454 [mmHg*s/mL], is replaced in theinflated, or stented model, by 80454 [mmHg*s/mL].

Reference is now made to FIG. 5 , which depicts a coronary tree model1010, a combination matrix 1020 depicting tree branch tags, and acombination matrix 1030 depicting tree branch resistances, all producedaccording to an example embodiment of the invention.

The tree model is an example tree model with nine branches, tagged withbranch numbers 0, 1 a, 1 b, 2 a, 2 b, 3 a, 3 b, 4 a and 4 b.

The combination matrix 1020 includes nine rows 1021-1029, which containdata about nine stream lines, that is, nine paths through which fluidcan flow through the tree model. Five of the rows 1025-1029 include datafor five full stream lines, in darker text, for five paths which go afull way to outlets of the tree model. Four of the rows 1021-1024include data for partial streamlines, in lighter text, for four pathswhich are not fully developed in the tree model, and do not go a fullway to outlets of the tree model.

The combination matrix 1030 depicts rows for the same tree model asdepicted in the combination matrix 1020, with branch resistances locatedin matrix cells corresponding to branch tags in the combination matrix1020.

After calculating a resistance of each branch, stream lines are definedfrom the tree origin, branch 0 to each outlet. To keep track of thestream lines, branches which constitute each stream line are listed in acombination matrix, as shown for example in FIG. 5 .

In some embodiments, defined stream lines are also numbered, as shown inFIG. 6 .

Reference is now made to FIG. 6 , which depicts a tree model 1100 of avascular system, with tags 1101-1105 numbering outlets of the tree model1100, produced according to an example embodiment of the invention, thetags corresponding to stream lines.

A pressure drop along a stream line j is calculated as a sum of pressuredrops at each of its component branches (i), according to:

dp _(j)=Σ

_(i) Q _(i)   Equation 5.7

when each branch has a different flow Q_(i).

Based on a principle of mass conservation at each bifurcation, the flowrate in a mother branch is the sum of flow rates of daughter branches.For example:

Q _(1a) =Q _(2a) +Q _(2b) =Q _(4a) +Q _(4b) +Q _(2b)   Equation 5.8

Thus, for example, a pressure drop along a stream line which ends atbranch 4 a is:

$\begin{matrix}{{dp}_{4a} = {{{\Re_{0}Q_{0}} + {\Re_{1a}Q_{1a}} + {\Re_{2a}Q_{2a}} + {\Re_{4a}Q_{4a}}}=={{\Re_{0}\left( {Q_{4a} + Q_{4b} + Q_{2b} + Q_{3a} + Q_{3b}} \right)} + {\Re_{1a}\left( {Q_{4a} + Q_{4b} + Q_{2b}} \right)} + {\Re_{2a}\left( {Q_{4a} + Q_{4b}} \right)} + {\Re_{4a}Q_{4a}}}=={{Q_{4a}\left( {\Re_{0} + \Re_{1a} + \Re_{2a} + \Re_{4a}} \right)} + {Q_{4b}\left( {\Re_{0} + \Re_{1a} + \Re_{2a}} \right)} + {Q_{2b}\left( {\Re_{0} + \Re_{1a}} \right)} + {Q_{3a}\Re_{0}} + {Q_{3b}\Re_{0}}}=={{Q_{4}{ER}_{4,4}} + {Q_{5}{ER}_{4,5}} + {Q_{1}{ER}_{4,1}} + {Q_{2}{ER}_{4,2}} + {Q_{3}{ER}_{4,3}}}}} & {{Equation}5.9}\end{matrix}$

where Q_(j) is a flow rate along stream line j, and ER_(4,j) is a sum ofcommon resistances of stream line j and stream line 4. A globalexpression is optionally formulated for the pressure drop along streamline j:

dp _(j) =ΣQ _(i) ER _(ij)   Equation 5.10

For a tree with k outlet branches, that is, for k full stream lines, aset of k linear equations are optionally used:

$\begin{matrix}{{\begin{bmatrix}{ER}_{11} & {ER}_{12} & \ldots & \ldots & {ER}_{1k} \\{ER}_{21} & {ER}_{22} & \ldots & \ldots & {ER}_{2k} \\\ldots & \text{ } & \text{ } & \text{ } & \text{ } \\{ER}_{k1} & {ER}_{k2} & \ldots & \ldots & {ER}_{kk}\end{bmatrix}\begin{bmatrix}Q_{1} \\Q_{2} \\\ldots \\\text{ } \\Q_{k}\end{bmatrix}} = \begin{bmatrix}{dp}_{1} \\{dp}_{2} \\\ldots \\\text{ } \\{dp}_{k}\end{bmatrix}} & {{Equation}5.11}\end{matrix}$${\overset{\_}{\overset{\_}{A}} \times \overset{\_}{Q}} = \overset{\_}{DP}$

where indices 1 . . . k represent stream lines in the tree, and Q₁ . . .Q_(k) represent flow rates at corresponding outlet branches. The k×kmatrix A consists of elements ER and is calculated from the combinationmatrix. For example, for the 5 stream lines tree shown in FIG. 6 , theER matrix is:

$\begin{matrix}{{ER} = \begin{bmatrix}{\Re_{0} + \Re_{1a} + \Re_{2b}} & \Re_{0} & \Re_{0} & {\Re_{0} + \Re_{1a}} & {\Re_{0} + \Re_{1a}} \\\Re_{0} & {\Re_{0} + \Re_{1b} + \Re_{3a}} & {\Re_{0} + \Re_{1b}} & \Re_{0} & \Re_{0} \\\Re_{0} & {\Re_{0} + \Re_{1b}} & {\Re_{0} + \Re_{1b} + \Re_{3b}} & \Re_{0} & \Re_{0} \\{\Re_{0} + \Re_{1a}} & \Re_{0} & \Re_{0} & {\Re_{0} + \Re_{1a} + \Re_{2a} + \Re_{4a}} & {\Re_{0} + \Re_{1a} + \Re_{2a}} \\{\Re_{0} + \Re_{1a}} & \Re_{0} & \Re_{0} & {\Re_{0} + \Re_{1a} + \Re_{2a}} & {\Re_{0} + \Re_{1a} + \Re_{2a} + \Re_{4b}}\end{bmatrix}} & {{Equation}5.12}\end{matrix}$ $\begin{matrix}{{ER} = \begin{bmatrix}56125 & 808 & 808 & 2731 & 2731 \\808 & 12997 & 2454 & 808 & 808 \\808 & 2454 & 57795 & 808 & 808 \\2731 & 808 & 808 & 95754 & 4300 \\2731 & 808 & 808 & 4300 & 55525\end{bmatrix}} & {{Equation}5.13}\end{matrix}$

Reference is now made to FIG. 23 , which shows an exemplary branchingstructure having recombining branches, according to some exemplaryembodiments of the invention. In some embodiments of the invention, aprovision is made for collateral and/or shunting vessels, where branchesrecombine in the tree.

In some embodiments of the invention, a streamline 2302, 2303 may bemodeled which comprises a loop: for example, the loop comprising branchsegments 2 a and 4 c, separating from and then recombining with branchsegment 2 b, or the loop comprising recombining branch segments 5 a and5 b. In some embodiments of the invention, the requisite termscorresponding to the branch may be written to reflect that the vascularresistances operate in parallel. Thus, for example, the pressure dropalong stream line 2302 may be written:

$\begin{matrix}{{dp}_{{4c} - {2b}} = {{\Re_{0}Q_{0}} + {\Re_{1a}Q_{1a}} + {\left( {\frac{1}{\Re_{2a} + \Re_{4c}} + \frac{1}{\Re_{2b}}} \right)^{- 1}\left( {Q_{4c} + Q_{2b}} \right)}}} & {{Equation}5.13b}\end{matrix}$

In some embodiments, fluid pressure measurements are made, for exampleblood pressure measurements. Based on provided fluid pressure boundaryconditions (P_(in) and P_(out_i)), a vector DP is defined, and Q_(i) iscalculated:

Q=A ⁻¹ ×DP    Equation 5.14

For example, for a constant pressure drop of 70 mmHg between the originand all the outlets, the following flow distribution between the outletsis calculated:

Q=[1.4356, 6.6946, 1.2754, 0.7999, 1.4282], where the units of flow aremL/s. The result is an output of the above method, and is depicted inFIG. 7 .

Reference is now made to FIG. 7 , which is a simplified illustration ofa vascular tree model 1210 produced according to an example embodimentof the invention, including branch resistances

_(i) 1220 [mmHg*s/mL] at each branch and a calculated flow Q_(i) 1230[mL/s] at each stream line outlet.

In some embodiments, two models of a tree are calculated—a first modelwith stenoses, optionally as measured for a specific patient, and asecond model without stenoses. FFR is calculated for each branch usingthe formula:

$\begin{matrix}{{FFR} = \frac{Q_{S}}{Q_{N}}} & {{Equation}5.15}\end{matrix}$

For example, for the tree described above, the FFR calculated for eachone of the 9 branches is:

FFR=[1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.8846 0.8874]

It should be emphasized that the above-calculated FFR, expresseddirectly in terms of flows Q_(S), Q_(N) (FFR_(flow)=Q_(S)/Q_(N)), isdistinct in its determination from the pressure measurement-derived FFR(FFR_(pressure)=P_(d)/P_(a)), calculated based on pressure differencesdistal P_(d) and proximal P_(a) to a stenosis. Furthermore, rather thanbeing a comparison of two variables of a fixed system state, it is acomparison of two distinct states of the system.

ForFFR_(pressure), a finding of a large difference in pressuremeasurements across a stenotic lesion (for example, FFR_(pressure)≤0.75)suggests that removing the lesion would remove a substantial resistance

to flow, whereby blood flow, in turn, would substantially increase.“Substantially”, in this case, means “enough to be medicallyworthwhile”. This chain of reasoning relies on simplifying assumptionsabout remaining pressures and resistances in the vascular systemdifferent in detail from those recited hereinabove in relation toFFR_(flow).

Nevertheless, the two indices are closely related in what they describe.FFR as such—although it is commonly measured by pressure differences ina fixed system state—is defined as the ratio of maximum blood flow in astenotic artery to maximum blood flow if the same artery were normal.Thus, FFR_(flow) and FFR_(pressure) may be characterized as differentlyarrived-at indexes of the same desired information: what fraction offlow can be restored, at least in principle, by intervention at aparticular region of the cardiac vasculature.

The acceptance of FFR_(pressure) by the field relates also to experienceand correlations with clinical outcome. There is, accordingly, apotential benefit to supplying a replacement for FFR_(pressure) whichdescribes the vascular system in terms medical professionals areaccustomed to. The index which FFR provides estimates the potential forrestoration of blood flow after treatment. It is a potential benefit,therefore, that FFR_(flow)—at least insofar as it comprises a ratio offlows—relates to the parameter of direct medical interest as closely asFFR_(pressure) itself, even though it is arrived at by a differentroute.

Also, as for FFR_(pressure), a goal of determining FFR_(flow), in someembodiments of the present invention, is the guidance of medicaldecision making by providing a rapidly calculable, easily interpreted,index. It is potentially sufficient for a medical professional seekingdiagnostic assistance to establish by a vascular index such asFFR_(flow) that intervention will make a medically meaningful change inperfusion. A ratio index is exemplary of an index that compactlyexpresses such change. It should also be noted that by describing anindex that expresses a potential for change, FFR_(flow), likeFFR_(pressure) itself, potentially reduces the effects of errors and/ordistraction in the absolute determination of vascular perfusioncharacteristics.

Quality of Results

Reference is now made to FIG. 24 , which is a Bland-Altman plot 2400 ofthe difference of FFR index (FFR_(pressure) and image-based FFR index(FFR_(flow)) as a function of their average, for some exemplaryembodiments of the invention.

In the exemplary graph, the mean difference of FFR index and image-basedFFR 2415 is −0.01 (N=34 lesions, from 30 patients), with the 2 standarddeviation lines 2420, 2425 found at 0.05 and −0.08. Lines 2405, 2410mark the typical cutoffs between preferably “non-treated” FFR(FFR>0.80), preferably “treated” FFR (FFR<0.75), and intermediate FFRvalues (0.075≤FFR≤0.80). On a linear correlation, the R² value was 0.85.Specificity was found to be 100%.

These validation results show that image-based FFR is potentially adirect substitute for FFR calculated from pressure measurement results.

In some embodiments of the invention, the relationship of FFR calculatedfrom images to a baseline FFR (for example, FFR measured by pressure, orFFR measured by contrast flow before and after actual stentimplantation) comprises a specificity of about, for example, 90%, 95%,100%, or another intermediate or smaller specificity. An R² valuedescribing correlation with a baseline FFR is about, for example, 0.75,0.80, 0.85, 0.90, or another larger, smaller, or intermediate value.

Some Example Implementations of Calculating an Index

In some embodiments of the invention, image processing techniques andnumerical calculations are combined for determining a physiologicalindex (for example, FFR_(flow)) which is functionally equivalent to thepressure-derived Fractional Flow Reserve (FFR (pressure)). In someembodiments, functional equivalence is direct; in some embodiments,achieving functional equivalence comprises the application of furthercalibration factors (representing, for example, an offset to vascularwidth, a change in blood viscosity, or simply an equivalence factorand/or function). The integration of the above-mentioned techniquespotentially enables providing a minimally invasive assessment of bloodflow during a diagnostic catheterization, and provides an appropriateestimation of the functional significance of coronary lesions.

In some embodiments of the invention, a novel physiological indexprovides a physiological index which potentially enables evaluating theneed for percutaneous coronary intervention, and supports makingreal-time diagnostic and interventional (treatment) decisions. Theminimal-invasive method potentially prevents unnecessary risk to apatient, and/or reduces time and/or cost of angiography, hospitalizationand/or follow-up.

In some embodiments, a scientific model, based on patient data, isprovided, which identifies geometrical characteristics of the patient'svascular system, comprising a vascular tree thereof, or even a singlevessel, and relevant hemodynamic information, equivalent in applicationto the present-day invasive FFR method (for example FFR_(flow)).

In addition, the model potentially allows examining a combination of 3Dreconstruction of the vessel and a numerical flow analysis to determinefunctional significance for a coronary lesion.

Some embodiments of the present invention perform 1-D reconstruction ofone or more coronary artery segments and/or branches during coronaryangiography, and a computational/numerical flow analysis in order toevaluate the arterial pressure, flow rate and/or flow resistancetherealong.

Some embodiments of the present invention perform 3-D reconstruction ofone or more coronary artery segments and/or branches during coronaryangiography, and a computational/numerical flow analysis in order toevaluate the arterial pressure, flow rate and/or flow resistancetherealong.

In embodiments of the invention in which the vascular function index iscalculated based only on the stenotic model, the resistance

_(S) contributed by a stenosis to the total resistance of the lesion'scrown is evaluated. The volume V_(crown) of the crown distal to thestenosis is also calculated. An FFR index (FFR_(resistance)) can then becalculated as a function which decreases with

_(S) and V_(crown). A representative example of such a functionincludes, without limitation,

$\begin{matrix}{{FFR} = \left( {1 + \frac{\Re_{S}kV_{crown}^{3/4}}{P_{a} - P_{0}}} \right)^{- 1}} & {{Equation}5.15a}\end{matrix}$

where P_(a) is the aortic pressure, P₀ is the pre-capillary pressure andk is a scaling law coefficient which can be adapted to the aorticpressure.

Reference is now made to FIG. 8 , which is a simplified flow chartillustration of an example embodiment of the invention. This embodimentis particularly useful when a vessel function index, such as FFR iscalculated based on two models of the vasculature.

FIG. 8 illustrates some portions of a method according to the exampleembodiment. The method includes receiving at least two 2-D angiographicimages of a portion of a coronary artery of a patient (1810) andreconstructing a 3-D tree model of a coronary artery system (1815), andif there is a lesion, including the lesion.

A flow analysis of blood flow and optionally arterial pressure along asegment of interest, based on the tree model and optionally on otheravailable hemodynamic measurements, such as aortic pressure and/oramount of injected contrast.

The example embodiment just described potentially provides aminimally-invasive physiological index indicative of functionalsignificance of coronary lesions.

The example method is optionally performed during a coronary angiographyprocedure, and calculations are optionally performed during the coronaryangiography procedure, such that the minimally-invasive physiologicalindex is provided in real-time.

Reference is now made to FIG. 9 , which is a simplified flow chartillustration of another example embodiment of the invention.

FIG. 9 illustrates a method for vascular assessment which includes:

producing a stenotic model of a subject's vascular system, the stenoticmodel comprising geometric measurements of the subject's vascular systemat one or more locations along a vessel centerline of at least onebranch of the subject's vascular system (1910);

obtain a flow characteristic of the stenotic model (1915);

producing a second model of a similar extent of the patient's vascularsystem as the stenotic model (1920);

obtaining the flow characteristic of the normal model (1925); and

calculating an index indicative of the need for revascularization, basedon the flow characteristic in the stenotic model and on the flowcharacteristic in the normal model (1930).

Reference is now made to FIG. 10 , which is a simplified flow chartillustration of yet another example embodiment of the invention.

FIG. 10 illustrates a method for vascular assessment which includes:

capturing a plurality of 2-D images of the subject's vascular system(2010);

producing a tree model of the subject's vascular system, the tree modelcomprising geometric measurements of the subject's vascular system atone or more locations along a vessel centerline of at least one branchof the subject's vascular system, using at least some of the pluralityof captured 2-D images (2015); and

producing a model of flow characteristics of the first tree model(2020).

Extents of the Coronary Tree Model

In some embodiments, the extent of a first, stenosed model is justenough to include a stenosis, a section of vessel proximal to thestenosis, and a section of vessel distal to the stenosis.

In such an embodiment the extent of the first model is, in some cases, asegment of a vessel between bifurcations, including a stenosis in thesegment. In some cases, especially when a stenosis is at a bifurcation,the extent includes the bifurcation, and sections of vessels proximaland distal to the stenosed bifurcation.

In some embodiments, an extent by which the first model extends proximalto the stenosis within a single segment is from as small as 1 or 2millimeters, up to as much as 20 to 50 millimeters, and/or up to as muchas the end of the segment itself.

In some embodiments, an extent by which the first model extends distalto the stenosis within a single segment is from as small as 1 or 2millimeters, up to as much as 20 to 50 millimeters, and/or up to as muchas the end of the segment itself.

In some embodiments, an extent by which the first model extends distalto the stenosis is measured by bifurcations of the vessel. In someembodiments, the first model extends distal to the stenosis by as few as1 or 2 bifurcations, and in some embodiments by as much as 3, 4, 5, andeven more bifurcations. In some embodiments the first model extendsdistal to the stenosis as far as resolution of the imaging processallows discerning distal portions of the vascular system.

A second model, of the same extent as the first model, is optionallyproduced, with the stenosis inflated as if the stenosis had beenrevascularized back to normal diameter.

Producing a Model of Physical Characteristics of a Vascular System

In an example implementation, given a proximal arterial pressure, P_(a),[mmHg], flow rate through a segment of interest Q_(s), [mL/s] isoptionally derived from a concentration of iodine contrast material,based on an analysis of concentration-distance-time curves, and ageometric description of the segment of interest, including diameterd(l) [cm], and/or volume V(l) [ml] as a function of segment length.

In some embodiments, especially in case of large vessels such as theLeft Anterior Descending coronary artery (LAD), blood flow can bemeasured for obtaining a flow model using a transthoracic echo Doppler,or other modalities such as MRI or SPECT.

For a given segment, a total resistance of the segment (R_(t),[mmHg*s/mL]) is optionally calculated by dividing arterial pressure byflow rate:

$\begin{matrix}{R_{t} = \frac{P_{a}}{Q_{s}}} & {{Equation}5.16}\end{matrix}$

where R_(t) corresponds to total resistance, P_(a) corresponds toarterial pressure, and Q_(s) corresponds to flow rate through the vesselsegment.

From geometric description of the segment, a local resistance of thestenosis in the segment R_(s), [mmHg*s/mL] is estimated. Estimation ofR_(s) may be made by any one or more of the following methods: using anempirical lookup table; and/or using a function such as described in theabove mentioned Kirkeeide reference; and/or by a cumulative summation ofPoiseuille resistances:

$\begin{matrix}{R_{s} = {\frac{128\mu}{\pi}{\int\frac{dl}{d^{4}}}}} & {{Equation}5.17}\end{matrix}$

where integration is over samples of the segment (dl), d is optionallyan arterial diameter of each sample, and μ is 0.035 g·cm⁻¹ s⁻¹,optionally blood viscosity.

The segment's downstream resistance is calculated for the segment R_(n),[mmHg*s/mL] as follows:

R _(t) =R _(t) −R _(s)   Equation 5.18

A normal flow through the segment without stenosis Q_(n), [mL/s], iscalculated for example as follows:

$\begin{matrix}{Q_{n} = \frac{P_{a}}{R_{n}}} & {{Equation}5.19}\end{matrix}$

where Q_(n) is an input flow to the segment, P_(a) is pressure proximalto the segment, and R_(n) is resistance to flow by vessels distal to thesegment.

Another form of Fractional Flow Reserve (FFR_(contrast-flow)) isoptionally derived as a ratio between measured flow rate through thestenosed segment and normal flow rate through the segment withoutstenosis:

$\begin{matrix}{{FFR} = \frac{Q_{s}}{Q_{n}}} & {{Equation}5.2}\end{matrix}$

In some embodiments, an index indicative of the potential effect ofrevascularization, such as an FFR index (for example,FFR_(contrast-flow)), is calculated using the data described below:

proximal arterial pressure P_(a), [mmHg] is measured;

a total inlet flow through a vessel origin, such as the coronary originQ_(total), [mL/s], is derived from a concentration of contrast material(such as iodine), optionally based on the analysis ofconcentration-distance-time curves. In some embodiments, especially forlarge vessels such as the Left Anterior Descending (LAD) coronaryartery, flow is optionally recorded using a transthoracic echo Dopplerand/or other modalities such as MRI and SPECT;

-   -   a subject's specific anatomy, including one or more of the        following:    -   a geometric description of arterial diameters along vessel tree        segments, for example up to 3-4 generations as a function of        segment length d(l) [cm];    -   a geometric description of arterial lengths along the vessel        tree segments (L_(i) [cm]), for example up to 1-2 generations        downstream of the segment of interest, and an accumulative crown        length (L_(crown) [cm]) downstream to the segment of interest:        L_(crown)=ΣL_(i);    -   a geometric description of arterial volumes along the vessel        tree segments V_(i) [ml], for example up to 1-2 generations        downstream of the segment of interest, and the accumulative        crown volume (V_(crown) [ml]) downstream to the segment of        interest: V_(crown)=ΣV_(i);    -   a myocardial mass (LV mass) distribution for the arterial        segment of interest M [ml] (in some embodiments LV mass is        optionally calculated using, for example, a transthoracic echo        Doppler);    -   and    -   a reference parameter K or function F which correlates anatomic        parameters such as described above with normal flow through the        segment (without stenosis) Q_(n), [mL/s], for example:

Q _(n) =K·M or Q _(n) =F(M)   Equation 5.21

Using the above data, the index indicative of the potential effect ofrevascularization, such as the FFR index, is optionally calculated byperforming the following calculations for each vessel segment underconsideration:

from the geometric parameter of the tree, such as length, volume, massand/or diameter, a normal flow Q_(n) in the segment is obtained;

from arterial pressure a resistance distal to the segment (R_(n),[mmHg*s/mL]) is calculated, for example as follows: R_(n)=P_(a)/Q_(n);

from geometry a local resistance of the stenosis in the segment R_(s),[mmHg*s/mL] is estimated, for example using one of the followingmethods:

a lookup table;

an empirical function such as described in the above mentioned Kirkeeidereference; and/or

a cumulative summation of Poiseuille resistancesR_(s)=(128μ)/π∫(dl)/(d⁴) where the integration is over samples of thesegment (dl), d is an arterial diameter of each sample, and μ is 0.035g·cm⁻¹·s⁻¹ is optionally blood viscosity;

the total resistance for the segment R_(t) [mmHg*s/mL] is optionallycalculated as: R_(t)=R_(n)+R_(s)

the flow through the stenosis segment Q_(s) [mL/s] is optionallycalculated as: Q_(s)=P_(a)/R_(t); and

the index, such as the fractional flow reserve (FFR), for the segment isoptionally calculated as: FFR=Q_(s)/Q_(n).

It is noted that a sanity check for the above calculation can optionallybe made by checking if the accumulated flow in the tree agrees with themeasured total flow as follows: Q_(total)=ΣQ_(i).

In some embodiments, the extent of the first model is such that itincludes a stenosis, and extends distally as far as resolution of theimaging modality which produced the vessel model allows, and/or severalbifurcations, for example 3 or 4 bifurcations distally to the stenosis.In some embodiments, the number of bifurcations is limited by theresolution to which vascular width can be determined from an image. Forexample, cutoff of the bifurcation order is set where the vascular widthis no longer determinable to within a precision of 5%, 10%, 15%, 20%, oranother larger, smaller, or intermediate precision. In some embodiments,sufficient precision is unavailable due, for example, to insufficientimaging resolution in the source images. Availability of a larger numberof measurable bifurcations is a potential advantage for fullerreconstruction of the detailed vascular resistance in the crown vesselsof a stenosis. It should be noted that in the current state of the art,CT scans generally provide a lower resolution than X-ray angiographicimaging, leading to a lowered availability of blood vessels from whichvascular resistances can be determined.

In an example implementation, a total inlet flow through a coronaryorigin is optionally derived from a concentration of contrast materialand optionally a subject's specific anatomy.

In some embodiments, data regarding the anatomy optionally includes ageometric description of arterial diameters along vessel segments up to3-4 bifurcations distally to a stenosis, a geometric description ofarterial lengths along vessel tree segments, a geometric description ofarterial volumes along the tree segments, and/or a myocardial mass (LVmass) distribution for the arterial segment of interest.

In some embodiments, data regarding the anatomy optionally includes ageometric description of arterial diameters along vessel segments as faras the imaging modality allows distally to a stenosis, a geometricdescription of arterial lengths along vessel tree segments, a geometricdescription of arterial volumes along the tree segments, and/or amyocardial mass (LV mass) distribution for the arterial segment ofinterest.

In some embodiments, LV mass is optionally calculated by using atransthoracic echo Doppler.

In some embodiments, a reference scaling parameter or function whichcorrelates anatomic parameters with normal flow through a segmentwithout stenosis is used.

In some embodiments, the extent of a first model includes a stenosedvessel, and a second model includes a similar extent of the vascularsystem, with a healthy vessel similar to the stenosed vessel.

An FFR index (FFR_(2-segment)) is optionally calculated from a ratiobetween the measured flow rate in a stenosed vessel, and a flow rate ina neighboring healthy vessel.

In some embodiments, the index is adjusted by a proportion between atotal length of vessels in the crowns of the stenosed vessel and thehealthy vessel. A crown of a vessel is hereby defined as a sub-treebranching off the vessel. The total length of a crown is optionallyderived from a 3-D reconstruction of the coronary tree.

Reference is now made to FIG. 11 , which is a simplified drawing 2100 ofvasculature which includes a stenosed vessel 2105 and a non-stenosedvessel 2107.

FIG. 11 depicts two vessels which are candidates for basing a flowcharacteristic comparison of the stenosed vessel 2105 and thenon-stenosed vessel 2107. FIG. 11 also depicts the stenosed vessel crown2115 and the non-stenosed vessel crown 2117.

It is noted that the two vessels in FIG. 11 seem to be especially goodcandidates for the comparison, since both seem to have similardiameters, and both seem to have similar crowns.

According to scaling laws, a linear relationship exists between a normalflow rate Q in an artery, and a total length of vessels in its crown.This relationship holds for both the neighboring healthy vessel, and thestenosed vessel. For a healthy vessel:

Q _(N) ^(h) =k·L ^(h)   Equation 6.1

where Q_(N) ^(h) is a flow rate of a healthy vessel, k is a correctionfactor, and L^(h) is a total length of crown vasculature of the healthyvessel.

For a stenosed vessel, similarly:

Q _(N) ^(s) =k·L ^(s)   Equation 6.2

where Q_(N) ^(s) is a flow rate of a stenosed vessel, k is a correctionfactor, and L^(s) is a total length of crown vasculature of the stenosedvessel.

FFR_(2-segment) is defined, in some embodiments, as a ratio between aflow rate in a stenosed artery during hyperemia, and the flow rate inthe same artery in the absence of the stenosis (normal flow rate), asdescribed by Equation 6.3 below. The above relationship yields a resultfor the FFR, calculated as a ratio between the measured flow rates inboth vessels divided by the ratio between their respective total crownlengths.

It is noted that scaling laws also state the relationship between thenormal flow rate and the total crown volume. In some embodiments, theindex or FFR is optionally calculated from the above-mentioned ratiobetween the measured flow rates in the sick and healthy arteries,divided by a ratio between respective total crown volumes of a stenosedvessel and a normal vessel respectively, to the power of ¾.

$\begin{matrix}{{{FFR} \equiv \frac{Q_{S}^{s}}{Q_{N}^{s}}} = {\frac{Q_{S}^{s}}{k \cdot L^{s}} = {\frac{Q_{S}^{s}}{\frac{Q_{N}^{h}}{L^{h}} \cdot L^{s}} = {\left( \frac{Q_{S}^{s}}{Q_{N}^{h}} \right) \cdot \frac{L^{h}}{L^{s}}}}}} & {{Equation}6.3}\end{matrix}$

Where Q_(N) ^(s) is an existing flow in a stenosed vessel, measured byany method described herein; Q_(N) ^(h) is an existing flow in a healthyvessel, measured by any method described herein; L^(s) is a total crownlength of the stenosed vessel; and L^(h) is a total crown length of thehealthy vessel.

The scaling laws also state a relationship between a normal flow rateand a total crown volume:

Q _(N) ^(h) =k _(v) ·V _(h) ^(3/4)   Equation 6.4

where Q_(N) ^(h) is a flow rate of a healthy vessel, k_(v) is acorrection factor, and V_(h) is a total volume of crown vasculature ofthe healthy vessel.

For a stenosed vessel, similarly:

Q _(N) ^(s) =k _(v) ·V _(s) ^(3/4)   Equation 6.5

where Q_(N) ^(s) is a flow rate of a stenosed vessel, k_(v) is acorrection factor, and V_(h) is a total volume of crown vasculature ofthe stenosed vessel.

An FFR is optionally calculated from the above-mentioned ratio betweenthe measured flow rates in the sick and healthy vessels, divided by theratio between the respective total crown volumes, raised to the power of¾:

$\begin{matrix}{{FFR} = {\left( \frac{Q_{S}^{s}}{Q_{N}^{h}} \right) \cdot \left( \frac{V_{h}}{V_{s}} \right)^{3/4}}} & {{Equation}6.6}\end{matrix}$

where V_(h) and V_(s) are measured, by way of a non-limiting example, byusing a 3-D model of the vasculature.

An Example Hardware Implementation

Reference is now made to FIG. 12A, which is a simplified illustration ofa hardware implementation of a system for vascular assessmentconstructed according to an example embodiment of the invention.

The example system of FIG. 12A includes:

Two or more imaging devices 2205 for capturing a plurality of 2-D imagesof the patient's vascular system; a computer 2210 functionally connected2209 to the two or more imaging devices 2205.

The computer 2210 is optionally configured to: accept data from theplurality of imaging devices 2205; produce a tree model of the patient'svascular system, wherein the tree model comprises geometric measurementsof the patient's vascular system at one or more locations along a vesselcenterline of at least one branch of the patient's vascular system,using at least some of the plurality of captured 2-D images; and producea model of flow characteristics of the tree model.

In some embodiments a synchronization unit (not shown) is used toprovide the imaging devices 2205 with a synchronization signal forsynchronizing the capturing of 2-D images of the patient's vascularsystem.

Reference is now made to FIG. 12B, which is a simplified illustration ofanother hardware implementation of a system for vascular assessmentconstructed according to an example embodiment of the invention.

The example system of FIG. 12B includes:

One imaging device 2235 for capturing a plurality of 2-D images of thepatient's vascular system and a computer 2210 functionally connected2239 to the imaging device 2235.

In the example embodiment of FIG. 12B, the imaging device 2235 isconfigured to obtain the 2-D images from two or more directions withrespect to the subject. The direction and location of the imaging device2235 with respect to the subject, and/or with respect to a fixed frameof reference, are optionally recorded, to aid in producing a vessel treemodel, whether 1 dimensional (1-D) or three dimensional (3-D), from 2-DImages taken by the imaging device 2235.

The computer 2210 is optionally configured to: accept data from theplurality of imaging device 2235; produce a tree model of the patient'svascular system, wherein the tree model comprises geometric measurementsof the patient's vascular system at one or more locations along a vesselcenterline of at least one branch of the patient's vascular system,using at least some of the plurality of captured 2-D images; and producea model of flow characteristics of the tree model.

In some embodiments a synchronization unit (not shown) is used toprovide the imaging device 2235 with a synchronization signal forsynchronizing the capturing of 2-D images of the patient's vascularsystem, optionally at a same phase during the cardiac cycle.

In some embodiments the computer 2210 accepts a subject's ECG signal(not shown), and selects 2-D images from the imaging device 2235according to the ECG signal, for example in order to select 2-D imagesat a same cardiac phase.

In some embodiments, the system of FIG. 12A or 12B includes an imageregistration unit which detects corresponding image features in the 2-Dimages; calculates image correction parameters based on thecorresponding image features; and registers the 2-D images so the imagefeatures correspond geometrically.

In some embodiments the image features are optionally selected as anorigin of the tree model; and/or a location of minimal radius in astenosed vessel; and/or a bifurcation of a vessel.

Exemplary System for Vascular State Scoring

Reference is now made to FIG. 22 , which is a simplified schematic of anautomatic VSST scoring system 700, according to some exemplaryembodiments of the invention.

In FIG. 22 , broad white pathways (for example, pathway 751) denotesimplified paths of data processing through the system. Broad blackpathways (for example, pathway 753) denote external data connections orconnections to the system user interface 720. Black pathway data contentis labeled by overlying trapezoidal blocks.

The vascular tree reconstructor 702, in some embodiments of theinvention, receives image data 735 from one or more imaging systems or asystem-connected network 730. Stenosis determiner 704, in someembodiments, determines the presence of stenotic vascular lesions basedon the reconstructed vascular tree. In some embodiments, metrics module706 determines additional metrics related to the disease state of thevascular tree, based on the reconstructed vascular tree and/ordetermined stenosis locations and other measurements.

In some embodiments, metrics extractor 701 comprises functions ofvascular tree reconstructor 702, stenosis determiner 704, and/or metricsmodule 706. In some embodiments, metrics extractor 701 is operable toreceive image data 735, and extract from it a plurality of vascularstate metrics, suitable, for example, as input to parameter compositor708.

In some embodiments, parameter compositor 708 converts determinedmetrics into subscore values (for example true/false values) whichcomprise parameters that “answer” vascular state scoring questions,and/or are otherwise are mapped to particular operations of a VSSTscoring procedure.

In some embodiments, subscore extractor 703 comprises functions ofvascular tree reconstructor 702, stenosis determiner 704, metrics module706, and/or parameter compositor 708. In some embodiments, subscoreextractor 703 comprises functions of metrics extractor 701. In someembodiments, subscore extractor 703 is operable to receive image data735, and extract from it one or more VSST subscores, suitable as inputfor score calculator 713.

Parameter finalizer 710, in some embodiments, ensures that parameterdata provided is sufficiently complete and correct to proceed to finalscoring. In some embodiments, corrections to automatically determinedparameters are determined at finalizer 710, optionally under operatorsupervision through system user interface 720. In some embodiments,lacunae in automatically provided parameter data are filled: forexample, by user input from system user interface 720; or by otherparameter data 725 provided, for example, from another diagnostic systemor a network providing access to clinical data.

Score compositor 712, in some embodiments, composes the finalizedoutputs into a weighted score output 715 based on the determinedparameters for the score. The score is made available, for example, overthe system user interface or to networked resources 730.

In some embodiments of the invention, score calculator 713 comprisesfunctions of the parameter finalizer 710 and/or score compositor 712. Insome embodiments, score calculator 713 is operable to receive compositedparameters and/or subscores (for example from parameter compositor 708and/or subscore extractor 703), and convert them to a VSST score output715.

In some embodiments of the invention, intermediate results of processing(for example, the reconstructed vascular tree, various metricsdetermined from it, and/or parameter determinations) are stored inpermanent or temporary storage on storage devices (not shown) of thesystem 700, and/or on a network 730.

The scoring system 700 has been described in the context of moduleswhich, in some embodiments of the invention, are implemented asprogrammed capabilities of a digital computer. It should be understoodthat the underlying system architecture may be implemented in variousways comprising embodiments of the invention; for example, as a singleor multiple-process application and/or as client-server processesrunning on the same or on different computer hardware systems. In someembodiments of the invention, the system is implemented in code forexecution by a general purpose processor. In some embodiments, part orall of the functionality of one or more modules is provided by an FPGAor another dedicated hardware component such as an ASIC.

To provide one example of a client-server configuration, a subscoreextractor 703 is implemented as a server process (or group ofserver-implemented processes) on one or more machines remote to a clientcomputer which implements modules such as the score calculator 713 anduser interface 720. It should be understood that other divisions ofmodules described herein (or even divisions within modules) areencompassed by some embodiments of the invention. A potential advantageof such a division may be, for example, to allow high-speed dedicatedhardware to perform computationally intensive portions of the scoring,while providing an economy of scale by allowing the hardware to beshared by multiple end-users. Such a distributed architecturepotentially also provides advantages for maintenance and/or distributionof new software versions.

Potential Benefits of Embodiments of the Invention

Some example embodiments of the invention are minimally invasive, thatis, they allow refraining from guidewire interrogation of the coronaryartery, and therefore minimize danger to a patient, compared to aninvasive FFR catheter procedure.

It is noted that an example embodiment of the invention enablesmeasuring a reliable index during catheterization, providing acost-effective way to potentially eliminate a need for processingangiographic data after the catheterization procedure, and/or foradditional equipment during the catheterization procedure, such as aguidewire, and/or for materials involved in a catheterization procedure,such as adenosine. It is noted that another potential saving includes asaving in hospitalization costs following better treatment decisions.

An example embodiment of the invention optionally enables trying outvarious post-inflation vessel cross sections in various post-inflationmodels of the vascular system, and selecting a suitable stent for asubject based on desired flow characteristics of the post-inflationmodel.

An example embodiment of the invention optionally automaticallyidentifies geometrical characteristics of a vessel, defines an outercontour of the vessel, and optionally provides relevant hemodynamicinformation associated with the vessel, corresponding to the present-dayinvasive FFR method.

An embodiment of the invention optionally generates an index indicativeof a need for coronary revascularization. The minimally invasiveembodiment of the invention potentially prevents unnecessary risk topatients, potentially reduces total time and cost of an angiography,hospitalization and follow-up.

A system constructed according to an example embodiment of the inventionpotentially enables shortening the diagnostic angiography procedure.Unnecessary coronary interventions during angiography and/or in thefuture are also potentially prevented. Also, a method according to anexample embodiment of the invention optionally enables assessment ofvascular problems in other arterial territories, such as carotidarteries, renal arteries, and diseased vessels of the limbs.

It is noted that resolution of angiographic images is typically higherthan resolution typically obtained by 3-D techniques such a CT. A modelconstructed from the higher resolution angiographic images can beinherently higher resolution, and provide greater geometric accuracy,and/or use of geometric properties of smaller vessels than CT images,and/or calculations using branching vessels distal to a stenosis formore generations, or bifurcations, downstream from the stenosis than CTimages.

A short list of potential non-invasive FFR benefits, one or more ofwhich are provided in some embodiments of the present invention,includes:

a non-invasive method which does not endanger the patient;

a computational method without additional time or invasive equipment;

a prognostic benefit in ‘borderline’ lesions and in multi-vesseldisease;

provides a reliable index to assess the need for coronaryrevascularization;

a method to assess and/or optimize revascularization procedures;

a strategy which saves cost of catheterization, hospitalization andfollow-up;

preventing unnecessary coronary interventions following angiography; and

a ‘one-stop shop’ comprehensive lesion assessment.

Another benefit of some present embodiments is the ability to produce atree model within a short period of time. This allows calculating ofindices, particularly but not necessarily, indices that are indicativeof vascular function (e.g., FFR) also within a short period of time(e.g., within less than 60 minutes or less than 50 minutes or less than40 minutes or less than 30 minutes or less than 20 minutes from the timeat which the 2-D images are received by the computer). Preferably, theindex is calculated while the subject is still immobilized on thetreatment surface for the porpoise of catheterization. Such fastcalculation of index is advantageous since it allows the physician toget the assessment for a lesion being diagnosed during thecatheterization procedure, and thus enable quick decision regarding theproper treatment for that lesion. The physician can determine thenecessity of treatment while being in the catheterization room, and doesnot have to wait for an offline analysis.

Additional advantages of fast calculation of index include reduced riskto the patient, ability to calculate the index without the need of drugsor invasive equipment, shortening of the duration of coronary diagnosticprocedure, established prognostic benefit in borderline lesions, reducedcosts, reduced number of unnecessary coronary interventions and reducedamount of subsequent procedures.

In this “game” of assessing the hemodynamic severity of each lesion, anon-real-time solution is often not a considered option. The physicianneeds to know whether to treat the lesion in the cath lab or not, andcan't afford to wait for an offline analysis. CT-based solutions arealso part of a different “game”, since the utilization of cardiac CTscans is low compared to PCI procedures, and the resolution, bothtemporal and spatial, is much lower compared to angiograms.

Another point to stress, is that the on-line image-based FFR evaluation,unlike the invasive evaluation, potentially allows assessment ofborderline lesions, and won't be necessarily limited to the percentageof lesions evaluated nowadays, since the risk to the patient (forexample, due to guidewire traversal), and the cost will be a lot lower.

It is expected that during the life of a patent maturing from thisapplication many relevant methods and systems for imaging a vascularsystem will be developed and the scope of the terms describing imagingare intended to include all such new technologies a priori.

As used herein the term “about” refers to 10%.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”.

The term “consisting of” means “including and limited to”.

The term “consisting essentially of” means that the composition, methodor structure may include additional ingredients, steps and/or parts, butonly if the additional ingredients, steps and/or parts do not materiallyalter the basic and novel characteristics of the claimed composition,method or structure.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

The words “example” and “exemplary” are used herein to mean “serving asan example, instance or illustration”. Any embodiment described as an“example or “exemplary” is not necessarily to be construed as preferredor advantageous over other embodiments and/or to exclude theincorporation of features from other embodiments.

The word “optionally” is used herein to mean “is provided in someembodiments and not provided in other embodiments”. Any particularembodiment of the invention may include a plurality of “optional”features unless such features conflict.

As used herein the term “method” refers to manners, means, techniquesand procedures for accomplishing a given task including, but not limitedto, those manners, means, techniques and procedures either known to, orreadily developed from known manners, means, techniques and proceduresby practitioners of the chemical, pharmacological, biological,biochemical and medical arts.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals therebetween.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting.

The invention is claimed as follows:
 1. A computer-implemented methodfor assessing a vascular function of a cardiac vasculature having astenotic segment, the method comprising calculating a flow indexquantifying the vascular function of the cardiac vasculature usingmedical images.